[draft] first capital

Thesis/README.md

@@ -1,15 +1,15 @@
-# Einleitung
+# Einleitung [draft]
 
-Smartphones enthalten immer mehr Sensoren, mit denen sie Daten aus ihrer Umwelt erfassen. Erst durch den Zugriff über Software werden diese Sensordaten zu nützlichen Features. So wird Beispielsweise das Display eingeschaltet sobald man das Smartphone aus der Hosentasche holt oder ausgeschaltet wenn man es sich ans Ohr hält. Ein großes Augenmerk erhält die Lokalisierung der Geräte. Hierdurch werden Anwendungen wie die Navigation ermöglicht. Bei der Distanzmessung geht es darum, die Strecke, die ein Gerät in Bewegung zurücklegt, zu erfassen. In dieser Arbeit soll untersucht werden, wie die Messung auf kleinen Skalen, im Zentimeterbereich umgesetzt werden kann.
+Smartphones enthalten immer mehr Sensoren, mit denen sie Daten aus ihrer Umwelt erfassen. Erst durch den Zugriff über Software werden diese Sensordaten zu nützlichen Features. So wird Beispielsweise das Display eingeschaltet, sobald man das Smartphone aus der Hosentasche holt oder ausgeschaltet, wenn man es sich ans Ohr hält. Ein großes Augenmerk erhält die Lokalisierung der Geräte. Hierdurch werden Anwendungen wie die Navigation ermöglicht. Bei der Distanzmessung geht es darum, die Strecke, die ein Gerät in Bewegung zurücklegt, zu erfassen. In dieser Arbeit soll untersucht werden, wie die Messung auf kleinen Skalen, im Zentimeterbereich, umgesetzt werden kann.
 
-## Motivation und Ausgangslage
+## Motivation und Ausgangslage [draft]
 
-Der Fokus dieser Arbeit liegt auf der Umsetzung der Distanzmessung mittels Bluetooth unter Verwendung des \ac{rssi}. Diese Lösung wird häufig in der Indoor-Navigation angewandt, da sie sowohl kostengünstig als auch weit verbreitet ist. Hierbei findet die Lokalisierung der Geräte zum Beispiel durch Triangulation mittels Referenzpunkten statt. Diese Reverenz punkte sind als Bluetoothbeacon bezeichnet. Sie können sowohl weitere Smartphones als auch dedizierte Hardware sein. Bei der Indoor-Navigation sind diese Referenzpunkte mehrere Meter auseinander, was zu einer geringen Signalstärke am Empfänger und somit zu größeren Auswirkungen von Störeinflüssen führt. Bei der Messung von kleinen Skalen können die Bluetooth Beacons in einem engeren Raster angeordnet werden. 
+Der Fokus dieser Arbeit liegt auf der Umsetzung der Distanzmessung mittels Bluetooth unter Verwendung des \ac{rssi}. Diese Lösung wird häufig in der Indoor-Navigation angewandt, da sie sowohl kostengünstig als auch weit verbreitet ist. Hierbei findet die Lokalisierung der Geräte zum Beispiel durch Triangulation mittels Referenzpunkten statt. Diese Reverenzpunkte werden als Bluetooth-Beacons bezeichnet. Sie können sowohl weitere Smartphones, als auch dedizierte Hardware sein. Bei der Indoor-Navigation sind diese Referenzpunkte mehrere Meter auseinander, was zu einer geringen Signalstärke am Empfänger und somit zu größeren Auswirkungen von Störeinflüssen führt. Bei der Messung von kleinen Skalen können die Bluetooth-Beacons in einem engeren Raster angeordnet werden. 
 
-## Zielsetzung
+## Zielsetzung [draft]
 
 Das Ziel der Arbeit ist es, die Genauigkeit einer Distanzmessung auf einem eingeschränkten Bereich von rund \SI{2}{\meter} zu erhöhen. Das gewählte Setup soll dabei möglichst einfach umsetzbar sein.
-Mithilfe einer Beispielimplementierung soll die Genauigkeit dieses Setups untersucht werden. Zur weiteren Verbesserung der Genauigkeit sollen verschiedene Filtermöglichkeiten implementiert werden. Mit einer genauen Distanzmessung lassen sich Beispielsweise neue Experimente mit dem Smartphone umsetzen.
+Mithilfe einer Beispielimplementierung soll die Genauigkeit dieses Setups untersucht werden. Zur weiteren Verbesserung der Genauigkeit sollen verschiedene Filtermöglichkeiten implementiert werden. Mit einer genauen Distanzmessung lassen sich beispielsweise neue Experimente mit dem Smartphone umsetzen.
 
 ---
 
@@ -27,57 +27,134 @@ Die Distanz beschreibt die Länge einer, durch eine dynamische Bewegung zurückg
 
 # Technische Grundlagen
 
-In diesem Artikel werden die Technischen Grundlagen erörtert und eine Abschließende Bewertung durchgeführt. Dabei werden die Grundlagen zunächst allgemein Betrachtet und in weiteren Kapiteln vertieft.
+In diesem Artikel werden die technischen Grundlagen erörtert und eine abschließende Bewertung durchgeführt. Dabei werden die Grundlagen zunächst allgemein betrachtet und in weiteren Kapiteln vertieft.
 
 ## Distanzmessung
 
-Die Distanzmessung beschreibt im Rahmen dieser Arbeit die Messung der Länge einer zurückgelegten Strecke. Dabei bezeichnet die Strecke den Weg zwischen Start- und Zielpunkt. Die digitale Erfassung einer Strecke basiert auf der Erfassung einzelner Wegpunkte [@Lerch_2006_BOOK S. 7 ff.]. Da zwischen den Wegpunkten keine Informationen vorliegen, wird dieser Zwischenraum als Gerade angenommen. Wie Abbildung \ref{fig:wegpunkte} verdeutlicht, wird die Streckenabbildung durch die Anzahl an aufgezeichneten Wegpunkten verbessert. 
+Die Distanzmessung beschreibt im Rahmen dieser Arbeit die Messung der Länge einer zurückgelegten Strecke. Dabei bezeichnet die Strecke den Weg zwischen Start- und Zielpunkt. Die digitale Erfassung einer Strecke basiert auf der Erfassung einzelner Wegpunkte [@Lerch_2006_BOOK, vgl. S. 7-8]. Da zwischen den Wegpunkten keine Informationen vorliegen, wird dieser Zwischenraum als Gerade angenommen. Wie Abbildung \ref{fig:wegpunktcount} verdeutlicht, wird die Streckenabbildung durch die Anzahl an aufgezeichneten Wegpunkten verbessert. Im linken Teil der Abbildung sind nur drei Messpunkte erfasst worden, der ermittelte Weg ergibt nahezu eine Gerade und entspricht nicht dem realen Weg. Im rechten Teil sind 8, gleichmäßig verteilte Messpunkte, der aufgezeichnete Weg entspricht fast dem realen Weg.
 
-![Auswirkung der Genauigkeit und Häufigkeit der Wegpunkterfassung \label{fig:wegpunkte}](../static/wegpunkte.png)
+![Auswirkung der Erfassungshäufigkeit von Wegpunkten. \label{fig:wegpunktcount}](../static/Wegpunkte.pdf)
 
 ## Lokalisierung
 
-Die Lokalisierung bezeichnet die genaue Position in einem 2D oder 3D Raum. Die verschiedene Methoden zur Lokalisierung werden in den Folgenden Kapiteln näher erläutert. 
+Zur Bestimmung der einzelnen Wegpunkte ist eine Lokalisierung des Messobjektes erforderlich. Hierbei wird die Position des Objekts im Raum bestimmt. Der Raum kann dabei eindimensional oder mehrdimensional sein [@Strang_2008_BOOK]. Die folgenden Kapitel erörtern verschiedene Verfahren zur Lokalisierung, bei denen die Position des oder der Sender bekannt ist und die Position des Empfängers ermittelt werden soll.
 
-### Anwesenheit und Räumliche Nähe
+### Cell-ID
 
-Die einfachste Form der Positionsbestimmung ist, zu Prüfen ob sich das Objekt in einem bestimmten Areal befindet. Hierbei ist die Auflösung vom Raster des Areals abhängig. Beim Einsatz von funkbasierten Systemen kann dieses Areal mehrere Zentimeter bis hin zu Kilometer groß sein. Die Genauigkeit lässt sich erhöhen, indem mehr Funksender kombiniert werden, deren Senderadius sich überlappt. 
+Zu den einfachsten Methoden der Lokalisierung gehört das \ac{cellid}-Verfahren. Dabei haben alle Sender einen eindeutig zugeordnete \ac{id}. Diese \ac{id} wird vom Sender mit ausgestrahlt. Der Empfangsbereich, in dem ein Sender empfangen werden kann, nennt sich Zelle (engl. Cell). Ein Empfänger, der das Signal empfängt, kann dieses durch die \ac{id} eindeutig einem Sender und dessen Zelle zuordnen [@Strang_2008_BOOK]. Dabei ist die Genauigkeit des Verfahrens im wesentlichen von der Reichweite, also der Größe der jeweiligen Zelle, des Senders abhängig. 
 
-### Entfernungsmessung
+Die Lokalisierung kann verbessert werden, wenn sich mehrere Sendezellen überlappen. Abbildung \ref{fig:cellid} rechts zeigt, das in diesem Fall die Position des Empfängers auf die Schnittmenge der Sendezellen begrenzt wird, die vom Empfänger empfangen werden. Der rötlich eingefärbte Bereich kennzeichnet das Areal in dem sich der Empfänger befinden kann. Die rote Begrenzung ist die Sendereichweite des Senders.
 
-Bei der Entfernungsmessung geht es darum den Abstand zwischen zwei Punkten zu ermitteln. Dies ist für viele Methoden die Grundlage zur Lokalisierung. 
+![Positionsbestimmung durch überschneidende Zellen. \label{fig:cellid}](../static/cellid.pdf)
+
+### Fingerprinting
+
+Das Fingerprinting ist ein Ansatz, der sich die Mehrwegausbreitung (mehr dazu unter Abschnitt \ref{fehlerkorrekturen}) von Funksignalen zu Nutze macht. Hierbei wird für jeden Empfangsort ein charakteristisches Muster (Fingerabdruck, engl. Fingerprint) aufgezeichnet [@Strang_2008_BOOK]. Dabei gliedert sich dieses Verfahren in zwei Phasen:
+
+1. Die Offline-Phase: Hierbei werden passende ortsabhängige Parameter bestimmt, durch die eine eindeutige Identifikation eines Ortes möglich ist. Diese Parameter werden für jeden Ort gemessen und in einer Datenbank mit der Ortsinformation verknüpft gespeichert. Die ortsabhängigen Parameter hängen stark von der Umgebung ab. Bei einer Umgebungsänderung müssen diese Parameter aktualisiert werden. 
+2. Die Online-Phase: Dabei misst der Empfänger den Fingerprint, also den Parameter zur Identifikation, und gleicht diesen mit der Datenbank ab. Dazu werden Mustererkennungsalgorithmen benötigt, welche aus der Datenbank den wahrscheinlichsten Fingerprint ermitteln und damit den wahrscheinlichsten Ort herausgeben.
+
+
+### Triangulation
+
+Die Triangulation basiert auf der Ermittlung des Einfallswinkels der eingehenden Signale. Dieses Verfahren wird auch \ac{aoa} genannt. Die Messung des Einfallswinkels ist mit gerichteten Antennenarrays oder Laufzeitmessungen zwischen mehreren Antennen möglich. Für den einfachen Fall einer Messung kann keine Entfernungsinformation gewonnen werden. Erst mehrere Messungen führen zu einem linearen Gleichungssystem, dessen Lösung die Position des Empfängers bestimmt [@Strang_2008_BOOK].
+
+![2D Positionierung über \acl{aoa}. \label{fig:aoa}](../static/aoaPositioning.pdf)
 
 ### Trilateration
 
+Bei der Lateration handelt es um ein Methode zur Positionsbestimmung bei der die Entfernung zwischen Sender und Empfänger ermittelt wird. Durch die Entfernung zwischen Sender und Empfänger entsteht im zweidimensionalen Bereich ein Kreis um den Sender. Der Empfänger befindet sich dann auf einem Punkt dieser Kreisbahn [@Strang_2008_BOOK]. Um eine eindeutige Position zu ermitteln sind mindestens drei Sender notwendig, weswegen diese Methode auch Trilateration genannt wird. Abbildung \ref{fig:lateration} zeigt das Verfahren. Die Position des Empfängers wurde zur besseren Darstellung nur eingekreist, er befindet sich auf dem Schnittpunkt der im inneren des gestrichelten schwarzen Kreis zu erkennen ist. Der Abstand zwischen Sender und Empfänger $r$ entspricht dem Radius des Kreises um den Sender. Der Empfänger befindet sich auf einem unbestimmten Punkt der Kreislinie. Wird nun ein weiterer Sender hinzugefügt, so definieren die jeweiligen Schnittpunkte der Kreise die mögliche Position des Empfängers. Bei drei Sendern gibt es im optimalen Fall nur einen Schnittpunkt bei dem alle drei Kreislinien aufeinander treffen. 
+
+![2D Positionierung mit der Trilateration \label{fig:lateration}](../static/lateration.pdf)
+
+Formel \ref{eq:lgsTrilateration} zeigt das allgemeine lineare Gleichungssystem zur Berechnung der Position bei der Trilateration [@Noertjahyana_2017]. Dabei beschreibt $x_i$ und $y_i$ die Position der Sender $i=1,2,3$ und $r_i$ den gemessenen Abstand zwischen Sender $i$ und Empfänger.
+
+\begin{equation}\label{eq:lgsTrilateration}
+\begin{aligned}
+  r_1^2= (x-x_1)^2 + (y-y_1)^2 \\
+  r_2^2= (x-x_2)^2 + (y-y_2)^2 \\
+  r_3^2= (x-x_3)^2 + (y-y_3)^2
+\end{aligned}
+\end{equation}
+
+Im weiteren werden die Verfahren zur Ermittlung der Entfernung zwischen Sender und Empfänger vorgestellt.
+
+#### Laufzeitmessung {-}
+
+Die Laufzeitmessung, besser bekannt unter dem englischen Begriff \ac{toa}, beruht auf der Messung der absoluten Signallaufzeit $t = t_i - t_0$ von einem Sender zum Empfänger. Dabei beschreibt $t_i$ die Sendezeit und $t_0$ den Empfangszeitpunkt des Signals. Zur Berechnung der Distanz $r$ wird die Lichtgeschwindigkeit $c$ mit der Laufzeit des Signals Multipliziert $r = c \cdot t$. Für diese Messung ist eine sehr genaue und zwischen Sender und Empfänger synchronisierte Zeiterfassung notwendig [@Strang_2008_BOOK]. 
+
+#### Laufzeitdifferenzmessung {-}
+
+Bei der Laufzeitdifferenzmessung, auch bekannt als \ac{tdoa}, wird die Differenz der Signallaufzeit zweier Sender am Empfänger gemessen. Der Vorteil gegenüber dem \ac{toa} Verfahren liegt darin, das keine Zeitsynchronizität zwischen dem Sender und Empfänger hergestellt werden muss. Die Laufzeitdifferenzen zwischen den Signalen zweier Sender entspricht damit einer Differenz der Distanz vom Empfänger zu den beiden Sendern [@Strang_2008_BOOK].
+
+#### Signalstärkemessung {-}
+
+Die Messung der Signalstärke, auch bekannt als \ac{rss} ist ein gängiges Verfahren bei der Lokalisierung mithilfe von Funksystemen [@Chen_2019; @Davidson_2017a; @Ye_2019]. Hierbei wird die Empfangsleistung und damit die Dämpfung des Signals am Empfänger gemessen. Dabei hängt die Signaldämpfung unter anderem von der Distanz zwischen Sender und Empfänger ab. Zur Berechnung der Entfernung ist die Kenntnis über den mathematischen Zusammenhang zwischen Entfernung und Signaldämpfung notwendig. Diese Ausbreitungsmodelle sind für viele Szenarien bekannt [@Strang_2008_BOOK].
+
+### Fazit
+
+Im folgenden sollen die eingangs erwähnten Verfahren zur Lokalisierung hinsichtlich der Fragestellung betrachtet werden. Dabei liegt ein besonderes Augenmerk auf der möglichen Ortsauflösung und dem Aufwand mit dem das Verfahren umgesetzt werden kann.
+
+Das \ac{cellid}-Verfahren hat eine sehr geringe Ortsauflösung. Auch mit einer hohen Anzahl an Sendern bleibt die ermittelte Position nur ein diffuses Areal anstelle einer Punktgenauen Lokalisierung. Der Aufwand der Umsetzung hingegen ist als eher gering ein zu schätzen.
+
+Beim Fingerprinting-Verfahren ist die Ortsauflösung unter anderem vom betriebenen Aufwand bei der Einrichtung abhängig. Auch die gewählten Parameter zum erstellen des Fingerabdrucks und die Beständigkeit der Umgebung haben großen Einfluss auf die Ortsauflösung. Daher muss die Einrichtung bei Veränderungen an der Umgebung erneut durchgeführt werden was den Aufwand für diese Methode stark erhöht.
+
+Das \acl{aoa}-Verfahren lässt sich nur umsetzen, wenn das Gerät die benötigte Hardware zur Ermittlung des Eintrittswinkel mitbringt. Die Ortsauflösung ist dann jedoch nur von den Messfehlern abhängig und kann somit zunächst als sehr hoch eingestuft werden. Der Aufwand ist jedoch, passende Hardware vorausgesetzt, relativ gering.
+
+Für die Trilateration stehen mehrere Verfahren zur Auswahl. Diese unterscheiden sich hauptsächlich im Aufwand. Die Ortsauflösung ist, wie schon beim \ac{aoa} Verfahren, abhängig von den Messfehlern der eingesetzten Verfahren. Dabei wird beim \ac{rss} Verfahren eine etwas geringere Ortsauflösung angenommen da die Entfernung aufgrund der Signalstärke nicht nur durch Umwelteinflüsse sondern auch durch das verwendete Modell beeinflusst wird. Der Aufwand für \ac{toa} und \ac{tdoa} wird mit sehr hoch angenommen da eine genaue Zeitmessung spezielle Hardware voraussetzt. Diese Hardware ist in Smartphones nicht verbaut.
+
+| Verfahren | mögliche Ortsauflösung | Aufwand |
+| ------- | ------------- | ------- |
+| \ac{cellid} | sehr gering | gering |
+| Fingerprinting | stark schwankend | sehr hoch |
+| \acl{aoa} | sehr hoch | gering |
+| \acl{toa} | sehr hoch | sehr hoch |
+| \acl{tdoa} | sehr hoch | sehr hoch |
+| \acl{rss} | hoch | gering |
+: Übersicht und Bewertung der Verfahren zur Lokalisierung \label{tab:location}
 
 ## Smartphonesensoren
 
-Aktuelle Smartphones besitzen eine viel zahl von Sensoren um mit ihrer Umwelt zu Interagieren. Viele der Sensoren lassen sich alleine oder in Kombination zur Entfernungsmessung oder auch Distanzmessung einsetzen [@Subbu_2013] [@Chen_2019] [@Li_2012] [@SosaSesma_2016]. 
+Aktuelle Smartphones besitzen eine Vielzahl von Sensoren, um mit ihrer Umwelt zu interagieren. Viele der Sensoren lassen sich alleine oder in Kombination zur Entfernungsmessung oder Distanzmessung einsetzen [@Subbu_2013; @Chen_2019; @Li_2012; @SosaSesma_2016]. 
 
 Die Entfernung zu einem Referenzpunkt wie einer Wand, lässt sich zum Beispiel durch den Einsatz eines Sonars messen. Für die Umsetzung kommen das Mikrofons und der Lautsprecher des Smartphones in Frage [@Graham_2015]. In dieser Arbeit geht es jedoch um einen flexibleren Einsatzbereich, bei dem eine Lokalisierung zwingend erforderlich ist.
 
 Der bekanntesten Sensoren zur Lokalisierung ist das \ac{gps}. Hierbei wird, mit Hilfe von Satelliten die Position des Smartphones ermittelt. Dies ermöglicht die Ortung außerhalb von Gebäuden mit einer Genauigkeit von wenigen Metern [@Bajaj_2002a]. Da die Messungen jedoch nicht auf den Außenbereich beschränkt sein sollen, wird \ac{gps} nicht näher betrachtet.
 
-Die Innenraum- Lokalisierung und Navigation ist ein Forschungsfeld mit großem Interesse. Viele Arbeiten basieren auf dem vom \ac{ieee} festgelegten Standard IEEE 802.11, besser bekannt als \ac{wifi} [@Chen_2019]. Für den Einsatz von \ac{wifi} zur Lokalisierung muss zunächst eine Karte mit der Funkstärkenverteilung erstellt werden [@Davidson_2017a]. Dies bedeutet einen hohen zeitlichen Aufwand bei der Einrichtung und eine geringe Flexibilität im Einsatz.
+Die Innenraum-Lokalisierung und Navigation ist ein Forschungsfeld mit großem Interesse. Viele Arbeiten basieren auf dem vom \ac{ieee} festgelegten Standard IEEE 802.11, besser bekannt als \ac{wifi} [@Chen_2019]. Für den Einsatz von \ac{wifi} zur Lokalisierung muss zunächst eine Karte (siehe Kapitel \ref{fingerprinting}) mit der Funkstärkenverteilung erstellt werden [@Davidson_2017a]. Dies bedeutet einen hohen zeitlichen Aufwand bei der Einrichtung und eine geringe Flexibilität im Einsatz.
 
-Ein weiterer Sensor der zur Lokalisierung in Innenräumen häufig betrachtet wird ist Bluetooth. Er ist weit verbreitet und kostengünstiger als \ac{wifi} [@Ye_2019]. Des weiteren wurde mit \ac{ble} ein Standard entwickelt der sehr stromsparend ist. Im weiteren Verlauf der Arbeit soll Bluetooth näher betrachtet.
+Ein weiterer Sensor, der zur Lokalisierung in Innenräumen häufig betrachtet wird, ist Bluetooth. Er ist weit verbreitet und kostengünstiger als \ac{wifi} [@Ye_2019]. Des weiteren wurde mit \ac{ble} ein Standard entwickelt, der sehr stromsparend ist. Im weiteren Verlauf der Arbeit soll Bluetooth näher betrachtet werden.
 
 ## Bluetooth
 
-Bei Bluetooth handelt es sich um einen durch die \ac{sig} entwickelten Industriestandard zur Datenübertragung über kurze Distanzen per Funktechnik. Bluetooth arbeitet im lizenzfreiem \ac{ism} von \SIrange{2,402}{2,480}{\giga\Hz}, dadurch darf es Weltweit zulassungsfrei betrieben werden. Mit Bluetooth 4.0 wurde \acl{ble} eingeführt. Dieses ist nicht abwärtskompatibel, bietet jedoch einige nützliche Besonderheiten. Ein Reduzierter Stromverbrauch und die kurze Aufbauzeit einer Übertragung sind die wesentlichen Vorteile. 
+Bei Bluetooth handelt es sich um einen, durch die \ac{sig} entwickelten, Industriestandard zur Datenübertragung über kurze Distanzen per Funktechnik. Bluetooth arbeitet im lizenzfreiem \ac{ism} von \SIrange{2,402}{2,480}{\giga\Hz}, dadurch darf es weltweit zulassungsfrei betrieben werden. Mit Bluetooth 4.0 wurde \acl{ble} eingeführt. Dieses ist nicht abwärtskompatibel, bietet jedoch einige nützliche Besonderheiten. Ein reduzierter Stromverbrauch und die kurze Aufbauzeit einer Übertragung sind die wesentlichen Vorteile. 
 
 \acl{ble} befindet sich im gleichen \ac{ism} wie das klassische Bluetooth. Es teilt den Frequenzbereich jedoch nicht in 79 Kanälen von \SI{1}{\mega\Hz} sondern in 40 Kanälen von je \SI{2}{\mega\Hz} auf.
 
 ### BLE und Entfernungsmessung
 
+- Mögliche Verfahren mit dem Smartphone
+
+### RSSI
+
+- RSSI - Verfahren
+- Formel für das RSSI BLE Scenario
+
 ## Messkette
 
-## Fehlerkorrekturen
+## Fehler
 
-### Fingerprinting
+### Systematische Fehler
+
+### Zufällige Fehler
+
+### Fehlerkorrekturen
 
 ### Filter
 
+Filterverfahren in Tabelle, Erklärung was Filter machen.
+Nicht alle müssen angewandt werden.
+
 ## Bewertung
 
 ## Beschreibung der eigenen Idee/ Innovation
@@ -108,7 +185,7 @@ Zone 3 ergibt sich aus dem, im Artikel [@Cho_2015a] beschriebenen maximal sinnvo
 
 Zu den Messpunkten in und am Rand der Zonen kommt ein spezieller Messpunkt. Dieser befindet sich auf einer Seite des Dreiecks und liegt somit genau zwischen zwei Beacon. Er wurde gewählt um den Einfluss des Smartphones auf die Funkstrecke der Beacon zu untersuchen.
 
-![Aufteilung des Versuchsaufbaus in Zonen und Messpunkte \label{fig:zones}](../static/zonen_und_messpunkte.png)
+![Aufteilung des Versuchsaufbaus in Zonen und Messpunkte \label{fig:zones}](../static/Zonen_und_Messpunkte.pdf)
 
 # Messauswertung
 
@@ -120,18 +197,4 @@ Zu den Messpunkten in und am Rand der Zonen kommt ein spezieller Messpunkt. Dies
 
 ## Ausblick
 
-# Abkürzungsverzeichnis
+# Literaturverzeichnis
-
-\begin{acronym}[ISM-Band]
-
-\acro{ble}[BLE]{Bluetooth Low Energy}
-\acro{gps}[GPS]{Global Positioning System}
-\acro{ieee}[IEEE]{Institute of Electrical and Electronics Engineers}
-\acro{ism}[ISM-Band]{Industrial, Scientific and Medical Band}
-\acro{rssi}[RSSI]{eceived Signal Strength Indicator}
-\acro{sig}[SIG]{Bluetooth Special Interest Group}
-\acro{wifi}[WiFi]{Wireless Fidelity}
-
-\end{acronym}
-
-# Quellenverzeichnis

Thesis/acronyms.yaml

@@ -0,0 +1,47 @@
+acronym:
+  longest: ISM-Band
+  list:
+    - id: aoa
+      short: AoA
+      long: Angle of Arrival
+    - id: ble
+      short: BLE
+      long: Bluetooth Low Energy
+    - id : cellid
+      short: Cell-ID
+      long: Cell Identification
+    - id: gps
+      short: GPS
+      long: Global Positioning System
+    - id: id
+      short: ID
+      long: Identifikator
+    - id: ieeeIEEE
+      long: Institute of Electrical and Electronics Engineers
+    - id: ism
+      short: ISM-Band
+      long: Industrial, Scientific and Medical Band
+    - id: rss
+      short: RSS
+      long: Received Signal Strength
+    - id: rssi
+      short: RSSI
+      long: Received Signal Strength Indicator
+    - id: sig
+      short: SIG
+      long: Bluetooth Special Interest Group
+    - id: tdoa
+      short: TDoA
+      long: Time Difference of Arrival
+    - id: toa
+      short: ToA
+      long: Time of Arrival
+    - id: wifi
+      short: WiFi
+      long: Wireless Fidelity
+    - id: aoa
+      short: AoA
+      long: Angle of Arrival
+    - id: wifi
+      short: WiFi
+      long: Wireless Fidelity

Thesis/metadata.yaml

@@ -0,0 +1,34 @@
+title: Distanzmessung auf kleinen Skalen mithilfe von Smartphone-Sensoren
+date: 01.02.2022
+hochschule: 
+  name: Wilhelm Büchner Hochschule	
+  adresse: Hilpertstraße 31, 64295 Darmstadt 
+logo: ../static/logo.png
+student:
+  name: Sebastian Preisner
+  email: wbh@calyrium.org
+  strasse: Koppelgasse 18
+  ort: 55270 Ober-Olm
+  matrikelnr: 900266
+studium:
+  fachbereich: Informatik
+  studiengang: Technische Informatik
+  studiengangnr: 1140
+aufgabe:
+  typ: Bachelorarbeit
+arbeit:
+  typ: Bachelorthesis 
+betreuer: 
+  - Michael Fleury
+  - Dr. Thomas Kalbe
+lang: de
+toc: true
+lof: true
+lot: true
+keywords:
+  - Lokalisierung
+  - Bluetooth
+  - Smartphone
+  - Sensoren
+  - Kalman Filter
+  - selbst korrigierend

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thesis.bib

@@ -35,8 +35,10 @@
 Previous efforts with indoor positioning systems concentrate on statistical fingerprinting methods, mainly using 802.11 (WLAN) as the platform. Some efforts have been made with purely signal strength based positioning, but indoor environments have shown to work
 unfavorably for these kinds of methods.},
   file           = {:files/391/Larsson - Distance estimation and positioning based on Bluet.pdf:PDF},
+  groups         = {Thesis von anderen},
   language       = {en},
   pages          = {37},
+  priority       = {prio1},
   qualityassured = {qualityAssured},
 }
 
@@ -54,6 +56,7 @@ unfavorably for these kinds of methods.},
   title    = {Can Bluetooth Be Used To Measure Distance?},
   year     = {2020},
   abstract = {"Bluetooth should be able to measure distance if it can connect two devices", I said to myself. But is that really simple? To answer that, I did my},
+  file     = {:files/399/can-bluetooth-measure-distance.html:URL},
   journal  = {Bluetooth Tech World},
   language = {en-GB},
   url      = {files/399/can-bluetooth-measure-distance.html},
@@ -78,18 +81,6 @@ unfavorably for these kinds of methods.},
   url      = {https://developers.google.com/android/exposure-notifications/ble-attenuation-computation?hl=de},
 }
 
-@Article{Cho_2015a,
-  author   = {Cho, Hosik and Ji, Jianxun and Chen, Zili and Park, Hyuncheol and Lee, Wonsuk},
-  title    = {Accurate Distance Estimation between Things: A Self-correcting Approach},
-  year     = {2015},
-  issn     = {2364-7108},
-  number   = {2},
-  pages    = {9},
-  volume   = {1},
-  abstract = {This paper suggests a method to measure the physical distance between an IoT device (a Thing) and a mobile device (also a Thing) using BLE (Bluetooth Low-Energy profile) interfaces with smaller distance errors. BLE is a well-known technology for the low-power connectivity and suitable for IoT devices as well as for the proximity with the range of several meters. Apple has already adopted the technique and enhanced it to provide subdivided proximity range levels. However, as it is also a variation of RSS-based distance estimation, Apple’s iBeacon could only provide immediate, near or far status but not a real and accurate distance. To provide more accurate distance using BLE, this paper introduces additional self-correcting beacon to calibrate the reference distance and mitigate errors from environmental factors. By adopting self-correcting beacon for measuring the distance, the average distance error shows less than 10% within the range of 1.5 meters. Some considerations are presented to extend the range to be able to get more accurate distances.},
-  language = {en},
-}
-
 @Misc{_2021a,
   month    = aug,
   title    = {Tracing App - distance measurement},
@@ -100,50 +91,26 @@ unfavorably for these kinds of methods.},
   url      = {files/406/tracing-measurement-tech-bluetooth.html},
 }
 
-@InCollection{Zhao_2020,
-  author    = {Zhao, Qingchuan and Wen, Haohuang and Lin, Zhiqiang and Xuan, Dong and Shroff, Ness and Park, Noseong and Sun, Kun and Foresti, Sara and Butler, Kevin and Saxena, Nitesh},
-  publisher = {Springer International Publishing},
-  title     = {On the Accuracy of Measured Proximity of Bluetooth-Based Contact Tracing Apps},
-  year      = {2020},
-  address   = {Cham},
-  pages     = {49--60},
-  volume    = {335},
-  abstract  = {A large number of Bluetooth-based mobile apps have been developed recently to help tracing close contacts of contagious COVID19 individuals. These apps make decisions based on whether two users are in close proximity (e.g., within 6 ft) according to the distance measured from the received signal strength (RSSI ) of Bluetooth. This paper provides a detailed study of the current practice of RSSI -based distance measurements among contact tracing apps by analyzing various factors that can affect the RSSI value and how each app has responded to them. Our analysis shows that configurations for the signal transmission power (TxPower ) and broadcasting intervals that affect RSSI vary significantly across different apps and a large portion of apps do not consider these affecting factors at all, or with quite limited tuning.},
-  issn      = {978-3-030-63085-0 978-3-030-63086-7},
-  journal   = {Security and Privacy in Communication Networks},
-  language  = {en},
-  url       = {https://link.springer.com/10.1007/978-3-030-63086-7_4},
-}
-
 @InProceedings{Thaljaoui_2015,
   author         = {Thaljaoui, Adel and Val, Thierry and Nasri, Nejah and Brulin, Damien},
-  title      = {BLE localization using RSSI measurements and iRingLA},
+  title          = {{BLE} localization using {RSSI} measurements and i{R}ing{LA}},
   year           = {2015},
+  month          = {mar},
   pages          = {2178--2183},
+  publisher      = {{IEEE}},
   abstract       = {Over the last few years, indoor localization has been a very dynamic research area that has drawn great attention. Many methods have been proposed for indoor positioning as well as navigation services. A big number of them were based on Radio frequency (RF) technology and Radio Signal Strength Indicator (RSSI) for their simplicity of use. The main issues of the studies conducted in this field are related to the improvement of localization factors like accuracy, computational complexity, easiness of deployment and cost. In our study, we used Bluetooth Low Energy (BLE) technology for indoor localization in the context of a smart home where an elderly person can be located using an hybrid system that combines radio, light and sound information. In this paper, we propose a model that averages the received signal strength indication (RSSI) at any distance domain which offered accuracy down to 0.4 meters, depending on the deployment configuration.},
-  doi        = {10.1109/ICIT.2015.7125418},
+  doi            = {10.1109/icit.2015.7125418},
   eventtitle     = {2015 IEEE International Conference on Industrial Technology (ICIT)},
+  file           = {:files/Thaljaoui et al. - 2015 - BLE localization using RSSI measurements and iRing.pdf:PDF},
+  groups         = {Thesis},
   journal        = {2015 IEEE International Conference on Industrial Technology (ICIT)},
   keywords       = {Accuracy, BLE, Bluetooth, Distance measurement, Estimation, Indexes, Localization, Position measurement, Receivers, RSSI, Smarthome},
+  priority       = {prio2},
+  qualityassured = {qualityAssured},
+  readstatus     = {read},
   url            = {files/410/7125418.html},
 }
 
-@InProceedings{AlQathrady_2017,
-  author     = {Al Qathrady, Mimonah and Helmy, Ahmed},
-  title      = {Improving BLE Distance Estimation and Classification Using TX Power and Machine Learning: A Comparative Analysis},
-  year       = {2017},
-  pages      = {79--83},
-  publisher  = {Association for Computing Machinery},
-  series     = {MSWiM '17},
-  abstract   = {Distance estimation and proximity classification techniques are essential for numerous IoT applications and in providing efficient services in smart cities. Bluetooth Low Energy (BLE) is designed for IoT devices, and its received signal strength indicator (RSSI) has been used in distance and proximity estimation, though they are noisy and unreliable. In this study, we leverage the BLE TX power level in BLE models.We adopt a comparative analysis framework that utilizes our extensive data library of measurements. It considers commonly used state-of-the-art model, in addition to our data-driven proposed approach. The RSSI and TX power are integrated into several parametric models such as log shadowing and Android Beacon library models, and machine learning models such as linear regression, decision trees, random forests and neural networks. Specific mobile apps are developed for the study experiment. We have collected more than 1.8 millions of BLE records between encounters with various distances that range from 0.5 to 22 meters in an indoor environment. Interestingly, considering TX power when estimating the distance reduced the mean errors by up to 46% in parametric models and by up to 35% in machine learning models. Also, the proximity classification accuracy increased by up to 103% and 70% in parametric and machine learning models, respectively. This work is one of the first studies (if not the first) that analyze in depth the TX power variations in improving the distance estimation and classification.},
-  doi        = {10.1145/3127540.3127577},
-  eventtitle = {Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems},
-  issn       = {978-1-4503-5162-1},
-  keywords   = {ble, distance estimation, iot, proximity classification},
-  shorttitle = {Improving BLE Distance Estimation and Classification Using TX Power and Machine Learning},
-  url        = {https://doi.org/10.1145/3127540.3127577},
-}
-
 @InProceedings{Lam_2019,
   author     = {Lam, Ching Hong and She, James},
   title      = {Distance Estimation on Moving Object using BLE Beacon},
@@ -160,29 +127,23 @@ unfavorably for these kinds of methods.},
 
 @InProceedings{Kaczmarek_2016,
   author         = {Kaczmarek, Mariusz and Ruminski, Jacek and Bujnowski, Adam},
-  title      = {Accuracy analysis of the RSSI BLE SensorTag signal for indoor localization purposes},
+  booktitle      = {Proceedings of the 2016 Federated Conference on Computer Science and Information Systems},
+  title          = {{A}ccuracy analysis of the {RSSI} {BLE} {S}ensor{T}ag signal for indoor localization purposes},
   year           = {2016},
+  month          = {oct},
   pages          = {1413--1416},
+  publisher      = {{IEEE}},
   abstract       = {In this paper we describe possibility of use the RSSI signal (Radio Signal Strength Indication) from Texas Instruments SensorTag CC2650 for indoor positioning purposes. This idea is not a new but in our opinion it is possible to use SensorTags with Bluetooth LE wireless interface for positioning inside buildings in such applications as people findings in hospitals, senior come care, etc. RSSI is mostly selected as the sensor localization method in the indoor circumstances. In this paper, we aim to analyze accuracy, calibrate and map RSSI to distance by doing a series of the experiments. Obtained results are very promising and shows possibility of use this technique for position estimation.},
+  doi            = {10.15439/2016f501},
   eventtitle     = {2016 Federated Conference on Computer Science and Information Systems (FedCSIS)},
+  groups         = {Thesis},
   journal        = {2016 Federated Conference on Computer Science and Information Systems (FedCSIS)},
   keywords       = {Bluetooth, IEEE 802.11 Standard, Mathematical model, Mobile handsets, Wireless communication, Wireless sensor networks},
+  priority       = {prio1},
+  qualityassured = {qualityAssured},
   url            = {files/418/7733434.html},
 }
 
-@InProceedings{Maratea_2018,
-  author     = {Maratea, Antonio and Gaglione, Salvatore and Angrisano, Antonio and Salvi, Giuseppe and Nunziata, Alessandro},
-  title      = {Non parametric and robust statistics for indoor distance estimation through BLE},
-  year       = {2018},
-  pages      = {1--6},
-  abstract   = {Indoor positioning through Smart Bluetooth (Bluetooth Low Energy or BLE) sensors is a promising new field, where noisy data and outliers make challenging even the simplest distance estimates. The power of the BLE signal is known to be highly unstable even when measurement conditions remain unchanged and statistics on repeated measurements are required in order to have a good confidence in the obtained short-range distance estimates. This work proposes a stack of corrections based on non-parametric and robust statistics as a preprocessing step on the measured data, such that both the calibration and the range estimation processes improve their accuracy. According to experiments, robust and non-parametric statistics are able to handle effectively the severe noise involved in RSSI measurements, reaching most of the times a sub-meter precision.},
-  doi        = {10.1109/EE1.2018.8385266},
-  eventtitle = {2018 IEEE International Conference on Environmental Engineering (EE)},
-  journal    = {2018 IEEE International Conference on Environmental Engineering (EE)},
-  keywords   = {Estimation, Wireless sensor networks, Calibration, Kernel, Meters, Pollution measurement, Power measurement},
-  url        = {files/420/8385266.html},
-}
-
 @InProceedings{Nguyen_2017,
   author     = {Nguyen, Quang Huy and Johnson, Princy and Nguyen, Trung Thanh and Randles, Martin},
   title      = {Optimized indoor positioning for static mode smart devices using BLE},
@@ -197,51 +158,6 @@ unfavorably for these kinds of methods.},
   url        = {files/424/8292666.html},
 }
 
-@Article{Ye_2019,
-  author   = {Ye, Feng and Chen, Ruizhi and Guo, Guangyi and Peng, Xuesheng and Liu, Zuoya and Huang, Lixiong},
-  journal  = {IEEE Access},
-  title    = {A Low-Cost Single-Anchor Solution for Indoor Positioning Using BLE and Inertial Sensor Data},
-  year     = {2019},
-  issn     = {2169-3536},
-  pages    = {162439--162453},
-  volume   = {7},
-  abstract = {Indoor positioning services have become necessary in many situations. Radio frequency (RF) signals are suitable for being used for positioning because of their ubiquity and imperceptibility. This paper utilizes the information from the baseband of a Bluetooth low energy (BLE) transceiver for angle estimation and signal strength for distance estimation. The scheme constitutes a single-anchor based solution to calculate the position of a client. It significantly reduces the cost of installation by avoiding traditional methods like multilateration or triangulation that require three or more anchors, even in a small space. To improve the performance, we design a fusion algorithm based on a Kalman filter to integrate measurements of the anchor station and simplified pedestrian dead reckoning (PDR) results from the client. Experiments show that the proposed solution estimates positions in high precision without initial user location or heading information. The mean error of the implementation is less than 1 m and can be improved to less than 0.5 m with a precise ranging measurement.},
-  doi      = {10.1109/ACCESS.2019.2951281},
-  groups   = {Methoden},
-  keywords = {BLE, Distance measurement, Estimation, Meters, Antenna arrays, Antenna measurements, data fusion, Fingerprint recognition, indoor positioning, Karman filter, pedestrian dead reckoning (PDR), single-anchor, Wireless fidelity},
-  url      = {files/427/8890682.html},
-}
-
-@Article{Chen_2019,
-  author   = {Chen, Jiayu and Chen, Hainan and Luo, Xiaowei},
-  journal  = {Automation in Construction},
-  title    = {Collecting building occupancy data of high resolution based on WiFi and BLE network},
-  year     = {2019},
-  issn     = {0926-5805},
-  pages    = {183--194},
-  volume   = {102},
-  abstract = {Building occupancy information is the premise of modern building service systems' control and energy conservation. Inaccurate occupancy information could result in a low comfort level and an energy waste. Existing occupancy detecting system relies on indirect and low-resolution environmental sensors, which potentially mislead facility managers and result in inefficiency in building energy use. In this study, the authors proposed a novel occupancy detection approach through a coupled indoor positioning system. The system integrates conventional k-nearest neighbor positioning algorithm and stochastic random walk algorithm to collect high-resolution occupancy data through Wi-Fi and Bluetooth Low Energy (BLE) networks. The proposed system is able to identify the meshed geospatial distribution of occupants, and to future track their movements in a network covered space. The detected occupancy meshes are suitable for direct implementation in building facility management since their operation is based on thermal zones rather than occupants' coordinates. To validate the feasibility and accuracy of the proposed system, the authors conducted a preliminary experiment in an institutional building. By comparing the positioning distance measurement metrics and matching parameters, the authors found the occupancy information detected by the proposed model is highly precise, accurate and reliable for the application in the building energy management.},
-  doi      = {10.1016/j.autcon.2019.02.016},
-  keywords = {Building energy system, Building occupant localization, kNN, Stochastic random walk, Zone-based},
-  language = {en},
-  url      = {files/429/S0926580518308744.html},
-}
-
-@Article{Fermani_2013,
-  author   = {Fermani, Francesco and Schönrich, Ralph},
-  journal  = {Monthly Notices of the Royal Astronomical Society},
-  title    = {A new calibration for the Blue Horizontal Branch},
-  year     = {2013},
-  issn     = {0035-8711},
-  number   = {2},
-  pages    = {1294--1301},
-  volume   = {430},
-  abstract = {We suggest a simple analytic approximation for magnitudes and hence distances of Blue Horizontal Branch (BHB) stars in sloan colours. Precedent formulations do not offer a simple closed formula, nor do they cover the full dependences, e.g., on metallicity. Furthermore, using BHB star samples from the Sloan Digital Sky Survey, we validate our distance calibration directly on field stars instead of globular clusters and assess the performance of other available distance calibrations. Our method to statistically measure distances is sufficiently accurate to measure the colour and metallicity dependence on our sample and can be applied to other sets of stars or filters.},
-  doi      = {10.1093/mnras/sts703},
-  groups   = {Methoden},
-  url      = {https://doi.org/10.1093/mnras/sts703},
-}
-
 @InProceedings{Shchekotov_2018,
   author     = {Shchekotov, Maksim and Shilov, Nikolay},
   title      = {Semi-Automatic Self-Calibrating Indoor Localization Using BLE Beacon Multilateration},
@@ -281,19 +197,6 @@ unfavorably for these kinds of methods.},
   url        = {files/442/7357741.html},
 }
 
-@InProceedings{Maratea_2019,
-  author     = {Maratea, Antonio and Salvi, Giuseppe and Gaglione, Salvatore},
-  title      = {Bagging to Improve the Calibration of RSSI Signals in Bluetooth Low Energy (BLE) Indoor Distance Estimation},
-  year       = {2019},
-  pages      = {657--662},
-  abstract   = {Originally conceived as proximity sensors, smart Bluetooth (Bluetooth Low Energy or BLE) beacons have been quickly adopted as inexpensive means to estimate distance of the transmitter from the receiver. Unfortunately the Received Signal Strength in unstable and produces such oscillations that right beyond a couple of meters the accurate estimation of distances becomes extremely challenging. In this paper, starting from a preprocessed RSSI vector of measurements, a Bootstrap Aggregating procedure is proposed to improve the calibration of RSSI signals. The proposed method, in combination with robust and non parametric statistics, reaches a sub-meter precision up to 6 meters of distance.},
-  doi        = {10.1109/SITIS.2019.00107},
-  eventtitle = {2019 15th International Conference on Signal-Image Technology Internet-Based Systems (SITIS)},
-  journal    = {2019 15th International Conference on Signal-Image Technology Internet-Based Systems (SITIS)},
-  keywords   = {BLE, Estimation, Wireless sensor networks, Calibration, Meters, Pollution measurement, Transmitters, Bagging, Bootstrap, Robust, WSN},
-  url        = {files/444/9067942.html},
-}
-
 @Misc{_2021b,
   month = aug,
   title = {Evaluation und Anwendung aktueller Entwickunglen im Bereich Bluetooth Low Energy am Beispiel von iBeacon},
@@ -386,15 +289,20 @@ unfavorably for these kinds of methods.},
 @Article{Paek_2016,
   author         = {Paek, Jeongyeup and Ko, JeongGil and Shin, Hyungsik},
   journal        = {Mobile Information Systems},
-  title    = {A Measurement Study of BLE iBeacon and Geometric Adjustment Scheme for Indoor Location-Based Mobile Applications},
+  title          = {{A} {M}easurement {S}tudy of {BLE} i{B}eacon and {G}eometric {A}djustment {S}cheme for {I}ndoor {L}ocation-{B}ased {M}obile {A}pplications},
   year           = {2016},
   issn           = {1574-017X},
   pages          = {e8367638},
   volume         = {2016},
   abstract       = {Bluetooth Low Energy (BLE) and the iBeacons have recently gained large interest for enabling various proximity-based application services. Given the ubiquitously deployed nature of Bluetooth devices including mobile smartphones, using BLE and iBeacon technologies seemed to be a promising future to come. This work started off with the belief that this was true: iBeacons could provide us with the accuracy in proximity and distance estimation to enable and simplify the development of many previously difficult applications. However, our empirical studies with three different iBeacon devices from various vendors and two types of smartphone platforms prove that this is not the case. Signal strength readings vary significantly over different iBeacon vendors, mobile platforms, environmental or deployment factors, and usage scenarios. This variability in signal strength naturally complicates the process of extracting an accurate location/proximity estimation in real environments. Our lessons on the limitations of iBeacon technique lead us to design a simple class attendance checking application by performing a simple form of geometric adjustments to compensate for the natural variations in beacon signal strength readings. We believe that the negative observations made in this work can provide future researchers with a reference on how well of a performance to expect from iBeacon devices as they enter their system design phases.},
   doi            = {10.1155/2016/8367638},
+  file           = {:files/Paek et al. - 2016 - A Measurement Study of BLE iBeacon and Geometric A.pdf:PDF},
+  groups         = {Thesis},
   language       = {en},
-  url      = {files/470/8367638.html},
+  priority       = {prio2},
+  publisher      = {Hindawi Limited},
+  qualityassured = {qualityAssured},
+  readstatus     = {read},
 }
 
 @Misc{_2021f,
@@ -457,17 +365,20 @@ unfavorably for these kinds of methods.},
 @Article{Ramirez_2021,
   author         = {Ramirez, Ramiro and Huang, Chien-Yi and Liao, Che-An and Lin, Po-Ting and Lin, Hsin-Wei and Liang, Shu-Hao},
   journal        = {Sensors},
-  title    = {A Practice of BLE RSSI Measurement for Indoor Positioning},
+  title          = {{A} {P}ractice of {BLE} {RSSI} {M}easurement for {I}ndoor {P}ositioning},
   year           = {2021},
   number         = {15},
   pages          = {5181},
   volume         = {21},
   abstract       = {Bluetooth Low Energy (BLE) is one of the RF-based technologies that has been utilizing Received Signal Strength Indicators (RSSI) in indoor position location systems (IPS) for decades. Its recent signal stability and propagation distance improvement inspired us to conduct this project. Beacons and scanners used two Bluetooth specifications, BLE 5.0 and 4.2, for experimentations. The measurement paradigm consisted of three segments, RSSI-distance conversion, multi-beacon in-plane, and diverse directional measurement. The analysis methods applied to process the data for precise positioning included the Signal propagation model, Trilateration, Modification coefficient, and Kalman filter. As the experiment results showed, the positioning accuracy could reach 10 cm when the beacons and scanners were at the same horizontal plane in a less-noisy environment. Nevertheless, the positioning accuracy dropped to a meter-scale accuracy when the measurements were executed in a three-dimensional configuration and complex environment. According to the analysis results, the BLE wireless signal strength is susceptible to interference in the manufacturing environment but still workable on certain occasions. In addition, the Bluetooth 5.0 specifications seem more promising in bringing brightness to RTLS applications in the future, due to its higher signal stability and better performance in lower interference environments.},
   doi            = {10.3390/s21155181},
-  groups   = {Methoden},
+  file           = {:files/Ramirez et al. - 2021 - A Practice of BLE RSSI Measurement for Indoor Posi.pdf:PDF},
+  groups         = {Methoden, Thesis},
   keywords       = {BLE, RSSI, IPS, Kalman filter, modification coefficient, trilateration},
   language       = {en},
-  url      = {files/486/htm.html},
+  priority       = {prio1},
+  qualityassured = {qualityAssured},
+  readstatus     = {read},
 }
 
 @Article{Abboud_,
@@ -514,7 +425,10 @@ unfavorably for these kinds of methods.},
   author   = {Thein, Myo Min},
   title    = {Degree of Bachelor of Science in Electrical and Computer Engineering},
   pages    = {86},
+  file     = {:files/Thein - Degree of Bachelor of Science in Electrical and Co.pdf:PDF},
+  groups   = {Thesis von anderen},
   language = {en},
+  priority = {prio1},
 }
 
 @Misc{_2021k,
@@ -528,7 +442,7 @@ unfavorably for these kinds of methods.},
 @Article{Miura_2015,
   author         = {Miura, Shunsuke and Kamijo, Shunsuke},
   journal        = {International Journal of Intelligent Transportation Systems Research},
-  title    = {GPS Error Correction by Multipath Adaptation},
+  title          = {{GPS} {E}rror {C}orrection by {M}ultipath {A}daptation},
   year           = {2015},
   issn           = {1868-8659},
   number         = {1},
@@ -538,6 +452,7 @@ unfavorably for these kinds of methods.},
   doi            = {10.1007/s13177-013-0073-9},
   groups         = {Methoden},
   language       = {en},
+  qualityassured = {qualityAssured},
   url            = {https://doi.org/10.1007/s13177-013-0073-9},
 }
 
@@ -558,12 +473,19 @@ unfavorably for these kinds of methods.},
   title          = {A Software-Based Sonar Ranging Sensor for Smart Phones},
   year           = {2015},
   issn           = {2327-4662},
+  month          = {dec},
   number         = {6},
   pages          = {479--489},
   volume         = {2},
   abstract       = {We live in a three dimensional world. However, the smart phones that we use every day are incapable of sensing depth, without the use of custom hardware. By creating new depth sensors, we can provide developers with the tools that they need to create immersive mobile applications that take advantage of the 3D nature of our world. In this paper, we propose a new sonar sensor for smart phones. This sonar sensor does not require any additional hardware, and utilizes the phone’s microphone and rear speaker. The sonar sensor calculates distances by measuring the elapsed time between the initial pulse and its reflection. We evaluate the accuracy of the sonar sensor by using it to measure the distance from the phone to an object. We found that we were able to measure the distances of objects accurately with an error bound of 12 centimeters.},
   doi            = {10.1109/JIOT.2015.2408451},
+  file           = {:files/Graham et al. - 2015 - A Software-Based Sonar Ranging Sensor for Smart Ph.pdf:PDF},
+  groups         = {Thesis, Verwendet},
   language       = {en},
+  priority       = {prio3},
+  publisher      = {Institute of Electrical and Electronics Engineers ({IEEE})},
+  qualityassured = {qualityAssured},
+  readstatus     = {read},
   url            = {http://ieeexplore.ieee.org/document/7054431/},
 }
 
@@ -608,20 +530,26 @@ unfavorably for these kinds of methods.},
   eventtitle = {2013 IEEE 10th Consumer Communications and Networking Conference (CCNC)},
   issn       = {978-1-4673-3133-3 978-1-4673-3131-9 978-1-4673-3132-6},
   journal    = {2013 IEEE 10th Consumer Communications and Networking Conference (CCNC)},
+  priority   = {prio1},
   url        = {http://ieeexplore.ieee.org/document/6488558/},
 }
 
 @Article{Abbas_2021,
   author         = {Abbas, Zahraa and Mosleh, Mahmood F.},
   journal        = {Advances in Mechanics},
-  title    = {Bluetooth Situating Utilizing Triangulation Strategies and RSSI},
+  title          = {{B}luetooth {S}ituating {U}tilizing {T}riangulation {S}trategies and {RSSI}},
   year           = {2021},
+  month          = jul,
   number         = {3},
   pages          = {376--386},
   volume         = {9},
   abstract       = {Area-based administrations are the most common applications on smartphones right now, and the trend is expected to continue. Indoor remote positioning is a key innovation that allows local agencies to work successfully indoors, where the Global Positioning System (GPS) has historically failed. Bluetooth has been commonly used in handheld devices such as tablets and PDAs. The economic potential of Bluetooth -based indoor positioning is immense. Received Signal Strength Indication (RSSI) may be used to remotely locate a computer. RSS will now be discovered without pre-association thanks to the latest Bluetooth protocol (as of version 2.1). This article initially discussed the general wireless positioning innovations field unit. At that stage, the RSS-based Bluetooth position with the most recent component is being considered. The numerical model is designed to investigate the interaction between RSS feeds as well as the differences between two Bluetooth systems. Bluetooth systems are located using three distance-based measurements: the Three-Boundary and Centroid Methods, as well as the Least Squares Estimation (LSE). The examination results are dissected, and techniques for improving position accuracy are examined.},
-  keywords = {LSE},
+  file           = {:files/Abbas und Mosleh - 2021 - Bluetooth Situating Utilizing Triangulation Strate.pdf:PDF},
+  keywords       = {LSE, RSSI, Bluetooth situating, Triangulation},
   language       = {en},
+  priority       = {prio3},
+  qualityassured = {qualityAssured},
+  readstatus     = {read},
   url            = {http://advancesinmech.com/index.php/am/article/view/131},
 }
 
@@ -637,15 +565,6 @@ unfavorably for these kinds of methods.},
   url      = {files/520/abstract.html},
 }
 
-@Misc{_2021l,
-  month    = aug,
-  title    = {Indoor Positioning Techniques using RSSI from Wireless Devices},
-  year     = {2021},
-  abstract = {The whole world is familiar with the Global Positioning System or GPS which can determine the exact location of any object with the help of satellite. But GPS signals are not available in indoors. To overcome this, Indoor Positioning System (IPS) is used which enables us to locate objects inside an indoor environment. Our goal is to build an Indoor Positioning System by estimating the location using Received Signal Strength Indication (RSSI) through wireless networks. The proposed model will determine the position of wireless devices in a room. We took the RSSI values as coordinates and specific reference points at every two meters making the room into a grid. The RSSI values on the reference point is measured. The position of the wireless devices will be estimated from the reference points using trilateration method and ITU indoor path loss model. With the aforementioned process we calculated the position of the wireless device using ITU indoor path loss model and trilateration. Using ITU indoor path loss model our mean error was 1.01166m, while using trilateration, it was 1.22m.},
-  language = {en-US},
-  url      = {files/522/9038591.html},
-}
-
 @InProceedings{Naveed_2019,
   author     = {Naveed, Munir and Javed, Yasir and Bhatti, Ghulam M. and Asif, Sayed},
   title      = {Smart indoor Positioning Model for Deterministic Environment},
@@ -672,18 +591,6 @@ unfavorably for these kinds of methods.},
   url        = {files/530/6413008.html},
 }
 
-@InProceedings{Sohan_2019,
-  author     = {Sohan, Asif Ahmed and Ali, Mohammad and Fairooz, Fabiha and Rahman, Adham Ibrahim and Chakrabarty, Amitabha and Kabir, Md. Rayhan},
-  title      = {Indoor Positioning Techniques using RSSI from Wireless Devices},
-  year       = {2019},
-  pages      = {1--6},
-  abstract   = {The whole world is familiar with the Global Positioning System or GPS which can determine the exact location of any object with the help of satellite. But GPS signals are not available in indoors. To overcome this, Indoor Positioning System (IPS) is used which enables us to locate objects inside an indoor environment. Our goal is to build an Indoor Positioning System by estimating the location using Received Signal Strength Indication (RSSI) through wireless networks. The proposed model will determine the position of wireless devices in a room. We took the RSSI values as coordinates and specific reference points at every two meters making the room into a grid. The RSSI values on the reference point is measured. The position of the wireless devices will be estimated from the reference points using trilateration method and ITU indoor path loss model. With the aforementioned process we calculated the position of the wireless device using ITU indoor path loss model and trilateration. Using ITU indoor path loss model our mean error was 1.01166m, while using trilateration, it was 1.22m.},
-  doi        = {10.1109/ICCIT48885.2019.9038591},
-  eventtitle = {2019 22nd International Conference on Computer and Information Technology (ICCIT)},
-  journal    = {2019 22nd International Conference on Computer and Information Technology (ICCIT)},
-  keywords   = {RSSI, Indoor Positioning, ITU, Trilateration, WiFi},
-}
-
 @Article{Konings_2019,
   author     = {Konings, Daniel and Alam, Fakhrul and Noble, Frazer and Lai, Edmund M.-K.},
   journal    = {IEEE Access},
@@ -715,17 +622,20 @@ unfavorably for these kinds of methods.},
 
 @InProceedings{SosaSesma_2016,
   author         = {Sosa-Sesma, Sergio and Perez-Navarro, Antoni},
-  title      = {Fusion system based on WiFi and ultrasounds for in-home positioning systems: The UTOPIA experiment},
+  booktitle      = {2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)},
+  title          = {{F}usion system based on {W}i{F}i and ultrasounds for in-home positioning systems: {T}he {UTOPIA} experiment},
   year           = {2016},
+  month          = {oct},
   pages          = {1--8},
+  publisher      = {{IEEE}},
   abstract       = {The research presented in this paper is focused on In-home positioning systems. These indoor environments are characterized by a reduced number of AP, narrow spaces and very important radio signal attenuation. Existing indoor techniques seems to be not totally suitable for these scenarios; this paper proposes an add-on for helping these techniques by the use of wearable ultrasound sensors (distance measurements). One important step for indoor techniques using RF signals is the off-line phase and the generation of Radio Maps. In order to considerably reduce the time needed for the off-line phase, this paper uses an existing tool for generating maps automatically. Therefore, a considerable gain of time is obtained. On the other hand, the combination of WiFi and range techniques allows us to do coarse and fine location. An IT artifact named UTOPIA (UlTrasOund Positioning Indoor App) has been developed to track all data needed for computing indoor position like RSSI, distances to walls, number of steps, heading and more. The project uses two maps for positioning: a WiFi map automatically generated; and an ultrasound map, that takes into account what should be the ultrasound value at every single point of the building and for several directions of the sensor. To get position, the values obtained by smartphone are compared with WiFi map. A particle filter technique is used to propagate position and ultrasound values are used to get the weight in the particle filter. Results obtained show that the methodology is coherent with real scenario and can be used for helping existing indoor position techniques in these specific scenarios (In-home environments). The main contributions of this paper are: 1) offering an alternative system suitable for in-home features that allows to calculate positioning by using WiFi and ultrasound; 2) avoiding the off-line mapping phase of WiFi fingerprinting by comparing with an automatically calculated WiFi map; and 3) using the map for improving positioning by generating an ultrasound reference map with the value of ultrasound signal at every single point of the building, and for several directions.},
   doi            = {10.1109/IPIN.2016.7743622},
   eventtitle     = {2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)},
-  groups     = {Methoden},
+  file           = {:files/Sosa-Sesma und Perez-Navarro - 2016 - Fusion system based on WiFi and ultrasounds for in.pdf:PDF},
-  journal    = {2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)},
+  groups         = {Methoden, Verwendet},
   keywords       = {IEEE 802.11 Standard, Mobile handsets, Sensors, Indoor Positioning, Android, IP networks, Arduino, Buildings, Inertial Sensors, Mobile device, Ultrasonic imaging, Ultrasonic variables measurement, Ultrasound Distance Sensors},
+  qualityassured = {qualityAssured},
   shorttitle     = {Fusion system based on WiFi and ultrasounds for in-home positioning systems},
-  url        = {files/540/7743622.html},
 }
 
 @Article{Forghani_2020,
@@ -763,6 +673,7 @@ unfavorably for these kinds of methods.},
   abstract = {Smartphone magnetometer readings exhibit high linear correlation when two phones coexist within a short distance. Thus, the detected coexistence can serve as a proxy for close human contact events, and one can conceive using it as a possible automatic tool to modernize the contact tracing in infectious disease epidemics. This paper investigates how good a diagnostic test it would be, by evaluating the discriminative and predictive power of the smartphone magnetometer-based contact detection in multiple measures. Based on the sensitivity, specificity, likelihood ratios, and diagnostic odds ratios, we find that the decision made by the smartphone magnetometer-based test can be accurate in telling contacts from no contacts. Furthermore, through the evaluation process, we determine the appropriate range of compared trace segment sizes and the correlation cutoff values that we should use in such diagnostic tests.},
   doi      = {10.1109/ACCESS.2019.2895075},
   keywords = {Meters, Sensors, Global Positioning System, Correlation, diagnostic test, human contact tracing, infectious disease epidemic, Infectious diseases, Magnetometers, Mobile sensing, smartphone magnetometer},
+  priority = {prio3},
   url      = {files/550/8626091.html},
 }
 
@@ -826,21 +737,27 @@ unfavorably for these kinds of methods.},
   groups     = {Methoden},
   journal    = {International Conference on Indoor Positioning and Indoor Navigation},
   keywords   = {Estimation, Sensors, Particle filters, Acceleration, indoor localization, Legged locomotion, Navigation, particle filter, PCA, PDR, Principal component analysis},
+  priority   = {prio2},
   url        = {files/560/6817854.html},
 }
 
 @InProceedings{Li_2012,
   author         = {Li, Fan and Zhao, Chunshui and Ding, Guanzhong and Gong, Jian and Liu, Chenxing and Zhao, Feng},
-  title      = {A reliable and accurate indoor localization method using phone inertial sensors},
+  booktitle      = {Proceedings of the 2012 {ACM} Conference on Ubiquitous Computing - {UbiComp} {\textquotesingle}12},
+  title          = {{A} reliable and accurate indoor localization method using phone inertial sensors},
   year           = {2012},
   pages          = {421--430},
-  publisher  = {Association for Computing Machinery},
+  publisher      = {{ACM} Press},
   series         = {UbiComp '12},
   abstract       = {This paper addresses reliable and accurate indoor localization using inertial sensors commonly found on commodity smartphones. We believe indoor positioning is an important primitive that can enable many ubiquitous computing applications. To tackle the challenges of drifting in estimation, sensitivity to phone position, as well as variability in user walking profiles, we have developed algorithms for reliable detection of steps and heading directions, and accurate estimation and personalization of step length. We've built an end-to-end localization system integrating these modules and an indoor floor map, without the need for infrastructure assistance. We demonstrated for the first time a meter-level indoor positioning system that is infrastructure free, phone position independent, user adaptive, and easy to deploy. We have conducted extensive experiments on users with smartphone devices, with over 50 subjects walking over an aggregate distance of over 40 kilometers. Evaluation results showed our system can achieve a mean accuracy of 1.5m for the in-hand case and 2m for the in-pocket case in a 31m×15m testing area.},
   doi            = {10.1145/2370216.2370280},
   eventtitle     = {Proceedings of the 2012 ACM Conference on Ubiquitous Computing},
+  file           = {:files/li2012.pdf:PDF},
+  groups         = {Verwendet},
   issn           = {978-1-4503-1224-0},
   keywords       = {indoor localization, inertial tracking, pedestrian model},
+  priority       = {prio2},
+  qualityassured = {qualityAssured},
   url            = {https://doi.org/10.1145/2370216.2370280},
 }
 
@@ -868,9 +785,10 @@ unfavorably for these kinds of methods.},
   abstract   = {Accelerometer-based biometric gait recognition offers a convenient way to authenticate users on their mobile devices. Modern smartphones contain in-built accelerometers which can be used as sensors to acquire the necessary data while the subjects are walking. Hence, no additional costs for special sensors are imposed to the user. In this publication we extract several features from the gait data and use the k-Nearest Neighbour algorithm for classification. We show that this algorithm yields a better biometric performance than the machine learning algorithms we previously used for classification, namely Hidden Markov Models and Support Vector Machines. We implemented the presented method on a smartphone and demonstrate that it is efficient enough to be applied in practice.},
   doi        = {10.1109/IIH-MSP.2012.11},
   eventtitle = {2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing},
+  file       = {:files/567/6274118.html:URL},
   journal    = {2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing},
   keywords   = {Legged locomotion, accelerometer, Authentication, biometrics, Error analysis, Feature extraction, gait recognition, Magnetic resonance, Mel frequency cepstral coefficient, smartphone, Vectors},
-  url        = {files/567/6274118.html},
+  priority   = {prio3},
 }
 
 @InProceedings{Hennecke_2011,
@@ -889,15 +807,19 @@ unfavorably for these kinds of methods.},
 @Article{Subbu_2013,
   author         = {Subbu, Kalyan Pathapati and Gozick, Brandon and Dantu, Ram},
   journal        = {ACM Transactions on Intelligent Systems and Technology},
-  title      = {LocateMe: Magnetic-fields-based indoor localization using smartphones},
+  title          = {{L}ocate{M}e: {M}agnetic-fields-based indoor localization using smartphones},
   year           = {2013},
   issn           = {2157-6904},
+  month          = {sep},
   number         = {4},
   pages          = {73:1--73:27},
   volume         = {4},
   abstract       = {Fine-grained localization is extremely important to accurately locate a user indoors. Although innovative solutions have already been proposed, there is no solution that is universally accepted, easily implemented, user centric, and, most importantly, works in the absence of GSM coverage or WiFi availability. The advent of sensor rich smartphones has paved a way to develop a solution that can cater to these requirements. By employing a smartphone's built-in magnetic field sensor, magnetic signatures were collected inside buildings. These signatures displayed a uniqueness in their patterns due to the presence of different kinds of pillars, doors, elevators, etc., that consist of ferromagnetic materials like steel or iron. We theoretically analyze the cause of this uniqueness and then present an indoor localization solution by classifying signatures based on their patterns. However, to account for user walking speed variations so as to provide an application usable to a variety of users, we follow a dynamic time-warping-based approach that is known to work on similar signals irrespective of their variations in the time axis. Our approach resulted in localization distances of approximately 2m--6m with accuracies between 80--100% implying that it is sufficient to walk short distances across hallways to be located by the smartphone. The implementation of the application on different smartphones yielded response times of less than five secs, thereby validating the feasibility of our approach and making it a viable solution.},
   doi            = {10.1145/2508037.2508054},
+  groups         = {Verwendet},
   keywords       = {smartphones, Indoor localization, magnetic fields, ubiquitous},
+  publisher      = {Association for Computing Machinery ({ACM})},
+  qualityassured = {qualityAssured},
   shorttitle     = {LocateMe},
   url            = {https://doi.org/10.1145/2508037.2508054},
 }
@@ -913,6 +835,7 @@ unfavorably for these kinds of methods.},
   abstract       = {The advancement of Internet of things (IoT) has revolutionized the field of telecommunication opening the door for interesting applications such as smart cities, resources management, logistics and transportation, wearables and connected healthcare. The emergence of IoT in multiple sectors has enabled the requirement for an accurate real time location information. Location-based services are actually, due to development of networks, sensors, wireless communications and machine learning algorithms, able to collect and transmit data in order to determine the target positions, and support the needs imposed by several applications and use cases. The performance of an indoor positioning system in IoT networks depends on the technical implementation, network architecture, the deployed technology, techniques and algorithms of positioning. This paper highlights the importance of indoor localization in internet of things applications, gives a comprehensive review of indoor positioning techniques and methods implemented in IoT networks, and provides a detailed analysis on recent advances in this field.},
   doi            = {10.5194/isprs-archives-xliv-4-w3-2020-121-2020},
   file           = {:files/isprs-archives-XLIV-4-W3-2020-121-2020.pdf:PDF},
+  priority       = {prio3},
   publisher      = {Copernicus {GmbH}},
   qualityassured = {qualityAssured},
 }
@@ -942,106 +865,6 @@ unfavorably for these kinds of methods.},
   file      = {:Files/rong-houwu2008.pdf:PDF},
 }
 
-@InCollection{Zhao_2020a,
-  author    = {Qingchuan Zhao and Haohuang Wen and Zhiqiang Lin and Dong Xuan and Ness Shroff},
-  booktitle = {Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering},
-  publisher = {Springer International Publishing},
-  title     = {On the Accuracy of Measured Proximity of Bluetooth-Based Contact Tracing Apps},
-  year      = {2020},
-  pages     = {49--60},
-  doi       = {10.1007/978-3-030-63086-7_4},
-  file      = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/407/Zhao et al. - 2020 - On the Accuracy of Measured Proximity of Bluetooth.pdf:PDF},
-}
-
-@InProceedings{Qathrady_2017,
-  author    = {Mimonah Al Qathrady and Ahmed Helmy},
-  booktitle = {Proceedings of the 20th {ACM} International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems},
-  title     = {{I}mproving {BLE} {D}istance {E}stimation and {C}lassification {U}sing {TX} {P}ower and {M}achine {L}earning},
-  year      = {2017},
-  month     = {nov},
-  publisher = {{ACM}},
-  doi       = {10.1145/3127540.3127577},
-  file      = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/413/Al Qathrady und Helmy - 2017 - Improving BLE Distance Estimation and Classificati.pdf:PDF},
-  groups    = {Bluetooth},
-}
-
-@InProceedings{Maratea_8014,
-  author   = {Antonio Maratea and Salvatore Gaglione and Antonio Angrisano and Giuseppe Salvi and Alessandro Nunziata and and Department of ScienceTechnologies and University of Naples “Parthenope and 80143 Naples and Italy},
-  title    = {Non parametric and robust statistics for indoor distance estimation through BLE},
-  year     = {8014},
-  abstract = {Indoor positioning through Smart Bluetooth (Blue- • Angle based: angular distance and triangulation are
-tooth Low Energy or BLE) sensors is a promising new field, used;
-where noisy data and outliers make challenging even the simplest
-distance estimates. The power of the BLE signal is known to • Range free: an accurate preliminary map of the inter-
-be highly unstable even when measurement conditions remain ested area with a prototype signal for each point in
-unchanged and statistics on repeated measurements are required the map is built, then similarity between points and
-in order to have a good confidence in the obtained short-range measured signal is used to locate objects.
-distance estimates. This work proposes a stack of corrections
-based on non-parametric and robust statistics as a preprocessing In the following, beacon sensors and range-based techniques
-step on the measured data, such that both the calibration have been chosen, using the Received Signal Strength Indicator
-and the range estimation processes improve their accuracy. (RSSI) as source.
-According to experiments, robust and non-parametric statistics
-are able to handle effectively the severe noise involved in RSSI Several corrections of the measured RSSI have been de-
-measurements, reaching most of the times a sub-meter precision. scribed in [11] within an optimized indoor positioning ap-},
-  file     = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/421/Maratea et al. - 2018 - Non parametric and robust statistics for indoor di.pdf:PDF},
-}
-
-@Article{Ye_2019a,
-  author    = {Feng Ye and Ruizhi Chen and Guangyi Guo and Xuesheng Peng and Zuoya Liu and Lixiong Huang},
-  journal   = {{IEEE} Access},
-  title     = {A Low-Cost Single-Anchor Solution for Indoor Positioning Using {BLE} and Inertial Sensor Data},
-  year      = {2019},
-  pages     = {162439--162453},
-  volume    = {7},
-  doi       = {10.1109/access.2019.2951281},
-  file      = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/426/Ye et al. - 2019 - A Low-Cost Single-Anchor Solution for Indoor Posit.pdf:PDF},
-  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
-}
-
-@Article{Chen_2019a,
-  author    = {Jiayu Chen and Hainan Chen and Xiaowei Luo},
-  journal   = {Automation in Construction},
-  title     = {Collecting building occupancy data of high resolution based on {WiFi} and {BLE} network},
-  year      = {2019},
-  month     = {jun},
-  pages     = {183--194},
-  volume    = {102},
-  doi       = {10.1016/j.autcon.2019.02.016},
-  file      = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/430/Chen et al. - 2019 - Collecting building occupancy data of high resolut.pdf:PDF},
-  publisher = {Elsevier {BV}},
-}
-
-@Article{Fermani_2013a,
-  author         = {Francesco Fermani and Ralph Schönrich},
-  journal        = {Monthly Notices of the Royal Astronomical Society},
-  title          = {{A} new calibration for the {B}lue {H}orizontal {B}ranch},
-  year           = {2013},
-  month          = {jan},
-  number         = {2},
-  pages          = {1294--1301},
-  volume         = {430},
-  doi            = {10.1093/mnras/sts703},
-  eprint         = {https://academic.oup.com/mnras/article/430/2/1294/2892440},
-  file           = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/432/Fermani und Schönrich - 2013 - A new calibration for the Blue Horizontal Branch.pdf:PDF},
-  publisher      = {Oxford University Press ({OUP})},
-  qualityassured = {qualityAssured},
-  timestamp      = {2021-08-11},
-}
-
-@InProceedings{Maratea_2019a,
-  author         = {Antonio Maratea and Giuseppe Salvi and Salvatore Gaglione},
-  title          = {{B}agging to {I}mprove the {C}alibration of {RSSI} {S}ignals in {B}luetooth {L}ow {E}nergy ({BLE}) {I}ndoor {D}istance {E}stimation},
-  year           = {2019},
-  month          = {nov},
-  organization   = {Department of Science and TechnologiesUniversity of Naples “Parthenope”},
-  publisher      = {{IEEE}},
-  abstract       = {Originally conceived as proximity sensors, smart Bluetooth (Bluetooth Low Energy or BLE) beacons have been quickly adopted as inexpensive means to estimate distance of the transmitter from the receiver. Unfortunately the Received Signal Strength in unstable and produces such oscillations that right beyond a couple of meters the accurate estimation of distances becomes extremely challenging. In this paper, starting from a preprocessed RSSI vector of measurements, a Bootstrap Aggregating procedure is proposed to improve the calibration of RSSI signals. The proposed method, in combination with robust and non parametric statistics, reaches a sub-meter precision up to 6 meters of distance.},
-  doi            = {10.1109/sitis.2019.00107},
-  file           = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/445/Maratea et al. - 2019 - Bagging to Improve the Calibration of RSSI Signals.pdf:PDF},
-  groups         = {Bluetooth, Distanzmessung, Algorithmen, Methoden},
-  qualityassured = {qualityAssured},
-}
-
 @InProceedings{Welch_1997a,
   author         = {Greg Welch and Gary Bishop},
   title          = {{A}n {I}ntroduction to the {K}alman {F}ilter},
@@ -1050,6 +873,7 @@ measurements, reaching most of the times a sub-meter precision. scribed in [11]
   abstract       = {In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation.},
   file           = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/580/Welch - 1997 - An Introduction to the Kalman Filter.pdf:PDF},
   groups         = {Distanzmessung, Algorithmen, Methoden},
+  priority       = {prio1},
   qualityassured = {qualityAssured},
 }
 
@@ -1077,6 +901,7 @@ measurements, reaching most of the times a sub-meter precision. scribed in [11]
   doi            = {10.1109/upec.2016.8114125},
   eprint         = {https://core.ac.uk/display/102700436},
   groups         = {Methoden},
+  priority       = {prio2},
   qualityassured = {qualityAssured},
 }
 
@@ -1098,11 +923,398 @@ estimation,  joint  estimation  and  dual  estimation.  Furthermore,  the  perfo
 configurations.},
   file           = {:files/27.pdf:PDF},
   groups         = {Methoden},
+  priority       = {prio1},
   qualityassured = {qualityAssured},
   timestamp      = {2021-10-28},
   url            = {https://core.ac.uk/display/102700436},
 }
 
+@Article{Ye_2019,
+  author         = {Ye, Feng and Chen, Ruizhi and Guo, Guangyi and Peng, Xuesheng and Liu, Zuoya and Huang, Lixiong},
+  journal        = {{IEEE} Access},
+  title          = {{A} {L}ow-{C}ost {S}ingle-{A}nchor {S}olution for {I}ndoor {P}ositioning {U}sing {BLE} and {I}nertial {S}ensor {D}ata},
+  year           = {2019},
+  issn           = {2169-3536},
+  pages          = {162439--162453},
+  volume         = {7},
+  abstract       = {Indoor positioning services have become necessary in many situations. Radio frequency (RF) signals are suitable for being used for positioning because of their ubiquity and imperceptibility. This paper utilizes the information from the baseband of a Bluetooth low energy (BLE) transceiver for angle estimation and signal strength for distance estimation. The scheme constitutes a single-anchor based solution to calculate the position of a client. It significantly reduces the cost of installation by avoiding traditional methods like multilateration or triangulation that require three or more anchors, even in a small space. To improve the performance, we design a fusion algorithm based on a Kalman filter to integrate measurements of the anchor station and simplified pedestrian dead reckoning (PDR) results from the client. Experiments show that the proposed solution estimates positions in high precision without initial user location or heading information. The mean error of the implementation is less than 1 m and can be improved to less than 0.5 m with a precise ranging measurement.},
+  doi            = {10.1109/ACCESS.2019.2951281},
+  file           = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/426/Ye et al. - 2019 - A Low-Cost Single-Anchor Solution for Indoor Posit.pdf:PDF},
+  groups         = {Methoden, Verwendet},
+  keywords       = {BLE, Distance measurement, Estimation, Meters, Antenna arrays, Antenna measurements, data fusion, Fingerprint recognition, indoor positioning, Karman filter, pedestrian dead reckoning (PDR), single-anchor, Wireless fidelity},
+  priority       = {prio2},
+  publisher      = {Institute of Electrical and Electronics Engineers ({IEEE})},
+  qualityassured = {qualityAssured},
+}
+
+@Article{Fermani_2013,
+  author         = {Fermani, Francesco and Schönrich, Ralph},
+  journal        = {Monthly Notices of the Royal Astronomical Society},
+  title          = {A new calibration for the Blue Horizontal Branch},
+  year           = {2013},
+  issn           = {0035-8711},
+  month          = {jan},
+  number         = {2},
+  pages          = {1294--1301},
+  volume         = {430},
+  abstract       = {We suggest a simple analytic approximation for magnitudes and hence distances of Blue Horizontal Branch (BHB) stars in sloan colours. Precedent formulations do not offer a simple closed formula, nor do they cover the full dependences, e.g., on metallicity. Furthermore, using BHB star samples from the Sloan Digital Sky Survey, we validate our distance calibration directly on field stars instead of globular clusters and assess the performance of other available distance calibrations. Our method to statistically measure distances is sufficiently accurate to measure the colour and metallicity dependence on our sample and can be applied to other sets of stars or filters.},
+  doi            = {10.1093/mnras/sts703},
+  eprint         = {https://academic.oup.com/mnras/article/430/2/1294/2892440},
+  file           = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/432/Fermani und Schönrich - 2013 - A new calibration for the Blue Horizontal Branch.pdf:PDF},
+  groups         = {Methoden},
+  publisher      = {Oxford University Press ({OUP})},
+  qualityassured = {qualityAssured},
+  timestamp      = {2021-08-11},
+  url            = {https://doi.org/10.1093/mnras/sts703},
+}
+
+@InProceedings{Maratea_2019,
+  author         = {Maratea, Antonio and Salvi, Giuseppe and Gaglione, Salvatore},
+  title          = {Bagging to Improve the Calibration of RSSI Signals in Bluetooth Low Energy (BLE) Indoor Distance Estimation},
+  year           = {2019},
+  month          = {nov},
+  organization   = {Department of Science and TechnologiesUniversity of Naples “Parthenope”},
+  pages          = {657--662},
+  publisher      = {{IEEE}},
+  abstract       = {Originally conceived as proximity sensors, smart Bluetooth (Bluetooth Low Energy or BLE) beacons have been quickly adopted as inexpensive means to estimate distance of the transmitter from the receiver. Unfortunately the Received Signal Strength in unstable and produces such oscillations that right beyond a couple of meters the accurate estimation of distances becomes extremely challenging. In this paper, starting from a preprocessed RSSI vector of measurements, a Bootstrap Aggregating procedure is proposed to improve the calibration of RSSI signals. The proposed method, in combination with robust and non parametric statistics, reaches a sub-meter precision up to 6 meters of distance.},
+  doi            = {10.1109/SITIS.2019.00107},
+  eventtitle     = {2019 15th International Conference on Signal-Image Technology Internet-Based Systems (SITIS)},
+  file           = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/445/Maratea et al. - 2019 - Bagging to Improve the Calibration of RSSI Signals.pdf:PDF},
+  groups         = {Bluetooth, Distanzmessung, Algorithmen, Methoden},
+  journal        = {2019 15th International Conference on Signal-Image Technology Internet-Based Systems (SITIS)},
+  keywords       = {BLE, Estimation, Wireless sensor networks, Calibration, Meters, Pollution measurement, Transmitters, Bagging, Bootstrap, Robust, WSN},
+  qualityassured = {qualityAssured},
+  url            = {files/444/9067942.html},
+}
+
+@Article{Chen_2019,
+  author         = {Chen, Jiayu and Chen, Hainan and Luo, Xiaowei},
+  journal        = {Automation in Construction},
+  title          = {{C}ollecting building occupancy data of high resolution based on {W}i{F}i and {BLE} network},
+  year           = {2019},
+  issn           = {0926-5805},
+  month          = {jun},
+  pages          = {183--194},
+  volume         = {102},
+  abstract       = {Building occupancy information is the premise of modern building service systems' control and energy conservation. Inaccurate occupancy information could result in a low comfort level and an energy waste. Existing occupancy detecting system relies on indirect and low-resolution environmental sensors, which potentially mislead facility managers and result in inefficiency in building energy use. In this study, the authors proposed a novel occupancy detection approach through a coupled indoor positioning system. The system integrates conventional k-nearest neighbor positioning algorithm and stochastic random walk algorithm to collect high-resolution occupancy data through Wi-Fi and Bluetooth Low Energy (BLE) networks. The proposed system is able to identify the meshed geospatial distribution of occupants, and to future track their movements in a network covered space. The detected occupancy meshes are suitable for direct implementation in building facility management since their operation is based on thermal zones rather than occupants' coordinates. To validate the feasibility and accuracy of the proposed system, the authors conducted a preliminary experiment in an institutional building. By comparing the positioning distance measurement metrics and matching parameters, the authors found the occupancy information detected by the proposed model is highly precise, accurate and reliable for the application in the building energy management.},
+  doi            = {10.1016/j.autcon.2019.02.016},
+  file           = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/430/Chen et al. - 2019 - Collecting building occupancy data of high resolut.pdf:PDF},
+  groups         = {Verwendet},
+  keywords       = {Building energy system, Building occupant localization, kNN, Stochastic random walk, Zone-based},
+  language       = {en},
+  publisher      = {Elsevier {BV}},
+  qualityassured = {qualityAssured},
+}
+
+@InProceedings{AlQathrady_2017,
+  author     = {Al Qathrady, Mimonah and Helmy, Ahmed},
+  booktitle  = {Proceedings of the 20th {ACM} International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems},
+  title      = {Improving BLE Distance Estimation and Classification Using TX Power and Machine Learning: A Comparative Analysis},
+  year       = {2017},
+  month      = {nov},
+  pages      = {79--83},
+  publisher  = {Association for Computing Machinery},
+  series     = {MSWiM '17},
+  abstract   = {Distance estimation and proximity classification techniques are essential for numerous IoT applications and in providing efficient services in smart cities. Bluetooth Low Energy (BLE) is designed for IoT devices, and its received signal strength indicator (RSSI) has been used in distance and proximity estimation, though they are noisy and unreliable. In this study, we leverage the BLE TX power level in BLE models.We adopt a comparative analysis framework that utilizes our extensive data library of measurements. It considers commonly used state-of-the-art model, in addition to our data-driven proposed approach. The RSSI and TX power are integrated into several parametric models such as log shadowing and Android Beacon library models, and machine learning models such as linear regression, decision trees, random forests and neural networks. Specific mobile apps are developed for the study experiment. We have collected more than 1.8 millions of BLE records between encounters with various distances that range from 0.5 to 22 meters in an indoor environment. Interestingly, considering TX power when estimating the distance reduced the mean errors by up to 46% in parametric models and by up to 35% in machine learning models. Also, the proximity classification accuracy increased by up to 103% and 70% in parametric and machine learning models, respectively. This work is one of the first studies (if not the first) that analyze in depth the TX power variations in improving the distance estimation and classification.},
+  doi        = {10.1145/3127540.3127577},
+  eventtitle = {Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems},
+  file       = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/413/Al Qathrady und Helmy - 2017 - Improving BLE Distance Estimation and Classificati.pdf:PDF},
+  groups     = {Bluetooth},
+  issn       = {978-1-4503-5162-1},
+  keywords   = {ble, distance estimation, iot, proximity classification},
+  shorttitle = {Improving BLE Distance Estimation and Classification Using TX Power and Machine Learning},
+  url        = {https://doi.org/10.1145/3127540.3127577},
+}
+
+@InCollection{Zhao_2020,
+  author    = {Zhao, Qingchuan and Wen, Haohuang and Lin, Zhiqiang and Xuan, Dong and Shroff, Ness and Park, Noseong and Sun, Kun and Foresti, Sara and Butler, Kevin and Saxena, Nitesh},
+  booktitle = {Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering},
+  publisher = {Springer International Publishing},
+  title     = {On the Accuracy of Measured Proximity of Bluetooth-Based Contact Tracing Apps},
+  year      = {2020},
+  address   = {Cham},
+  pages     = {49--60},
+  volume    = {335},
+  abstract  = {A large number of Bluetooth-based mobile apps have been developed recently to help tracing close contacts of contagious COVID19 individuals. These apps make decisions based on whether two users are in close proximity (e.g., within 6 ft) according to the distance measured from the received signal strength (RSSI ) of Bluetooth. This paper provides a detailed study of the current practice of RSSI -based distance measurements among contact tracing apps by analyzing various factors that can affect the RSSI value and how each app has responded to them. Our analysis shows that configurations for the signal transmission power (TxPower ) and broadcasting intervals that affect RSSI vary significantly across different apps and a large portion of apps do not consider these affecting factors at all, or with quite limited tuning.},
+  doi       = {10.1007/978-3-030-63086-7_4},
+  file      = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/407/Zhao et al. - 2020 - On the Accuracy of Measured Proximity of Bluetooth.pdf:PDF},
+  issn      = {978-3-030-63085-0 978-3-030-63086-7},
+  journal   = {Security and Privacy in Communication Networks},
+  language  = {en},
+  url       = {https://link.springer.com/10.1007/978-3-030-63086-7_4},
+}
+
+@InProceedings{Maratea_2018,
+  author     = {Maratea, Antonio and Gaglione, Salvatore and Angrisano, Antonio and Salvi, Giuseppe and Nunziata, Alessandro},
+  title      = {Non parametric and robust statistics for indoor distance estimation through BLE},
+  year       = {2018},
+  pages      = {1--6},
+  abstract   = {Indoor positioning through Smart Bluetooth (Bluetooth Low Energy or BLE) sensors is a promising new field, where noisy data and outliers make challenging even the simplest distance estimates. The power of the BLE signal is known to be highly unstable even when measurement conditions remain unchanged and statistics on repeated measurements are required in order to have a good confidence in the obtained short-range distance estimates. This work proposes a stack of corrections based on non-parametric and robust statistics as a preprocessing step on the measured data, such that both the calibration and the range estimation processes improve their accuracy. According to experiments, robust and non-parametric statistics are able to handle effectively the severe noise involved in RSSI measurements, reaching most of the times a sub-meter precision.},
+  doi        = {10.1109/EE1.2018.8385266},
+  eventtitle = {2018 IEEE International Conference on Environmental Engineering (EE)},
+  file       = {:/home/sebastian/Dokumente/Privat/Studium/WBH/JabRef/files/421/Maratea et al. - 2018 - Non parametric and robust statistics for indoor di.pdf:PDF},
+  journal    = {2018 IEEE International Conference on Environmental Engineering (EE)},
+  keywords   = {Estimation, Wireless sensor networks, Calibration, Kernel, Meters, Pollution measurement, Power measurement},
+  url        = {files/420/8385266.html},
+}
+
+@Misc{_2021l,
+  author     = {Sohan, Asif Ahmed and Ali, Mohammad and Fairooz, Fabiha and Rahman, Adham Ibrahim and Chakrabarty, Amitabha and Kabir, Md. Rayhan},
+  month      = aug,
+  title      = {Indoor Positioning Techniques using RSSI from Wireless Devices},
+  year       = {2021},
+  abstract   = {The whole world is familiar with the Global Positioning System or GPS which can determine the exact location of any object with the help of satellite. But GPS signals are not available in indoors. To overcome this, Indoor Positioning System (IPS) is used which enables us to locate objects inside an indoor environment. Our goal is to build an Indoor Positioning System by estimating the location using Received Signal Strength Indication (RSSI) through wireless networks. The proposed model will determine the position of wireless devices in a room. We took the RSSI values as coordinates and specific reference points at every two meters making the room into a grid. The RSSI values on the reference point is measured. The position of the wireless devices will be estimated from the reference points using trilateration method and ITU indoor path loss model. With the aforementioned process we calculated the position of the wireless device using ITU indoor path loss model and trilateration. Using ITU indoor path loss model our mean error was 1.01166m, while using trilateration, it was 1.22m.},
+  doi        = {10.1109/ICCIT48885.2019.9038591},
+  eventtitle = {2019 22nd International Conference on Computer and Information Technology (ICCIT)},
+  journal    = {2019 22nd International Conference on Computer and Information Technology (ICCIT)},
+  keywords   = {RSSI, Indoor Positioning, ITU, Trilateration, WiFi},
+  language   = {en-US},
+  pages      = {1--6},
+  url        = {files/522/9038591.html},
+}
+
+@Book{BergerGrabner_2016_BOOK,
+  author         = {Berger-Grabner, Doris},
+  publisher      = {Springer-Verlag GmbH},
+  title          = {{W}issenschaftliches {A}rbeiten in den {W}irtschafts- und {S}ozialwissenschaften},
+  year           = {2016},
+  isbn           = {9783658130787},
+  month          = sep,
+  ean            = {9783658130787},
+  file           = {:files/balzert.pdf:PDF},
+  groups         = {Books},
+  pagetotal      = {246},
+  priority       = {prio1},
+  qualityassured = {qualityAssured},
+  url            = {https://www.ebook.de/de/product/27954104/doris_berger_grabner_wissenschaftliches_arbeiten_in_den_wirtschafts_und_sozialwissenschaften.html},
+}
+
+@Article{Cho_2015a,
+  author         = {Ho-sik Cho and Jianxun Ji and Zili Chen and Hyuncheol Park and Wonsuk Lee},
+  journal        = {{O}pen {J}ournal of {I}nternet {O}f {T}hings ({OJIOT})},
+  title          = {{A}ccurate {D}istance {E}stimation between {T}hings: {A} {S}elf-correcting {A}pproach},
+  year           = {2015},
+  issn           = {2364-7108},
+  number         = {2},
+  pages          = {19--27},
+  volume         = {1},
+  abstract       = {This paper suggests a method to measure the physical distance between an IoT device (a Thing) and a mobile device (also a Thing) using BLE (Bluetooth Low-Energy profile) interfaces with smaller distance errors. BLE is a well-known technology for the low-power connectivity and suitable for IoT devices as well as for the proximity with the range of several meters. Apple has already adopted the technique and enhanced it to provide subdivided proximity range levels. However, as it is also a variation of RSS-based distance estimation, Apple's iBeacon could only provide immediate, near or far status but not a real and accurate distance. To provide more accurate distance using BLE, this paper introduces additional self-correcting beacon to calibrate the reference distance and mitigate errors from environmental factors. By adopting self-correcting beacon for measuring the distance, the average distance error shows less than 10\% within the range of 1.5 meters. Some considerations are presented to extend the range to be able to get more accurate distances.},
+  file           = {:files/Cho et al. - 2015 - Accurate Distance Estimation between Things A Sel.pdf:PDF},
+  groups         = {Thesis, Verwendet},
+  priority       = {prio1},
+  qualityassured = {qualityAssured},
+  ranking        = {rank4},
+  readstatus     = {read},
+  url            = {http://nbn-resolving.de/urn:nbn:de:101:1-201704244959},
+}
+
+@InProceedings{Akcan_2006a,
+  author         = {Hüseyin Akcan and Vassil Kriakov and Herv{\'{e}} Brönnimann and Alex Delis},
+  booktitle      = {Proceedings of the 5th {ACM} international workshop on Data engineering for wireless and mobile access - {MobiDE} {\textquotesingle}06},
+  title          = {{GPS}-{F}ree node localization in mobile wireless sensor networks},
+  year           = {2006},
+  publisher      = {{ACM} Press},
+  doi            = {10.1145/1140104.1140113},
+  groups         = {Thesis},
+  priority       = {prio2},
+  qualityassured = {qualityAssured},
+}
+
+@Article{Yu_2015a,
+  author         = {Minghe Yu and Guoliang Li and Ting Wang and Jianhua Feng and Zhiguo Gong},
+  journal        = {{IEEE} Transactions on Knowledge and Data Engineering},
+  title          = {{E}fficient {F}iltering {A}lgorithms for {L}ocation-{A}ware {P}ublish/{S}ubscribe},
+  year           = {2015},
+  issn           = {1041-4347},
+  month          = apr,
+  number         = {4},
+  pages          = {950--963},
+  volume         = {27},
+  abstract       = {Location-based services have been widely adopted in many systems. Existing works employ a pull model or user-initiated model, where a user issues a query to a server which replies with location-aware answers. To provide users with instant replies, a push model or server-initiated model is becoming an inevitable computing model in the next-generation location-based services. In the push model, subscribers register spatio-textual subscriptions to capture their interests, and publishers post spatio-textual messages. This calls for a high-performance location-aware publish/subscribe system to deliver publishers' messages to relevant subscribers. In this paper, we address the research challenges that arise in designing a location-aware publish/subscribe system. We propose an R-tree based index by integrating textual descriptions into R-tree nodes. We devise efficient filtering algorithms and effective pruning techniques to achieve high performance. Our method can support both conjunctive queries and ranking queries. We discuss how to support dynamic updates efficiently. Experimental results show our method achieves high performance which can filter 500 messages in a second for 10 million subscriptions on a commodity computer.},
+  doi            = {10.1109/tkde.2014.2349906},
+  file           = {:files/yu2015.pdf:PDF},
+  publisher      = {Institute of Electrical and Electronics Engineers ({IEEE})},
+  qualityassured = {qualityAssured},
+}
+
+@Article{Bajaj_2002a,
+  author         = {Bajaj, R. and Ranaweera, S.L. and Agrawal, D.P.},
+  journal        = {Computer},
+  title          = {{GPS}: location-tracking technology},
+  year           = {2002},
+  number         = {4},
+  pages          = {92--94},
+  volume         = {35},
+  doi            = {10.1109/mc.2002.993780},
+  file           = {:files/bajaj2002.pdf:PDF},
+  groups         = {Thesis, Verwendet},
+  publisher      = {Institute of Electrical and Electronics Engineers ({IEEE})},
+  qualityassured = {qualityAssured},
+  readstatus     = {skimmed},
+}
+
+@Article{Davidson_2017a,
+  author         = {Pavel Davidson and Robert Piche},
+  journal        = {{IEEE} Communications Surveys {\&} Tutorials},
+  title          = {{A} {S}urvey of {S}elected {I}ndoor {P}ositioning {M}ethods for {S}martphones},
+  year           = {2017},
+  issn           = {1553-877X},
+  number         = {2},
+  pages          = {1347--1370},
+  volume         = {19},
+  doi            = {10.1109/comst.2016.2637663},
+  file           = {:files/davidson2016.pdf:PDF},
+  groups         = {Thesis, Verwendet},
+  priority       = {prio1},
+  publisher      = {Institute of Electrical and Electronics Engineers ({IEEE})},
+  qualityassured = {qualityAssured},
+  url            = {https://ieeexplore.ieee.org/abstract/document/7782316},
+}
+
+@InCollection{Cho_2014a,
+  author         = {Seong Yun Cho},
+  booktitle      = {Lecture Notes in Geoinformation and Cartography},
+  publisher      = {Springer International Publishing},
+  title          = {{R}ange {D}omain {IMM} {F}iltering with {A}dditional {S}ignal {A}ttenuation {E}rror {M}itigation of {I}ndividual {C}hannels for {WLAN} {RSSI}-{B}ased {P}osition-{T}racking},
+  year           = {2014},
+  isbn           = {978-3-319-11878-9},
+  month          = {nov},
+  pages          = {91--104},
+  doi            = {10.1007/978-3-319-11879-6_7},
+  ean            = {9783319118789},
+  groups         = {Thesis},
+  pagetotal      = {296},
+  priority       = {prio1},
+  qualityassured = {qualityAssured},
+  url            = {https://www.ebook.de/de/product/22762371/progress_in_location_based_services_2014.html},
+}
+
+@Article{Cheng_2014a,
+  author         = {Jiantong Cheng and Ling Yang and Yong Li and Weihua Zhang},
+  journal        = {Physical Communication},
+  title          = {{S}eamless outdoor/indoor navigation with {WIFI}/{GPS} aided low cost {I}nertial {N}avigation {S}ystem},
+  year           = {2014},
+  month          = {dec},
+  pages          = {31--43},
+  volume         = {13},
+  abstract       = {This paper describes an integrated navigation system that can be used for pedestrian
+navigation in both outdoor and indoor environments. With the aid of Global Positioning
+System (GPS) positioning solutions, an Inertial Navigation System (INS) can provide stable
+and continuous outdoor navigation. When moving indoors, WIFI positioning can replace
+the GPS in order to maintain the integrated system’s long-term reliability and stability.
+On the other hand, the position from an INS can also provide apriori information to
+aid WIFI positioning. Signal strength-based WIFI positioning is widely used for indoor
+navigation. A new fingerprinting method is proposed so as to improve the performance
+of WIFI stand-alone positioning. For pedestrian navigation applications, a step detection
+method is implemented to constrain the growth of the INS error using an Extend Kalman
+Filter (EKF). Experiments have been conducted to test this system and the results have
+demonstrated the feasibility of this seamless outdoor/indoor navigation system.},
+  comment        = {Artikel zum Zeigen das man die Bluetoothnavigation durch Inertial Sensoren verbessern könnte.},
+  doi            = {10.1016/j.phycom.2013.12.003},
+  file           = {:files/cheng2014.pdf:PDF},
+  keywords       = {Indoor Navigation, WIFI Positioning, Fingerprinting, Step Detection, Pedestrian Navigation},
+  priority       = {prio1},
+  publisher      = {Elsevier {BV}},
+  qualityassured = {qualityAssured},
+}
+
+@InProceedings{10.1007/978-3-642-12654-3_3,
+  author    = {Kj{\ae}rgaard, Mikkel Baun and Blunck, Henrik and Godsk, Torben and Toftkj{\ae}r, Thomas and Christensen, Dan Lund and Gr{\o}nb{\ae}k, Kaj},
+  booktitle = {Pervasive Computing},
+  title     = {{I}ndoor {P}ositioning {U}sing {GPS} {R}evisited},
+  year      = {2010},
+  address   = {Berlin, Heidelberg},
+  editor    = {Flor{\'e}en, Patrik and Kr{\"u}ger, Antonio and Spasojevic, Mirjana},
+  pages     = {38--56},
+  publisher = {Springer Berlin Heidelberg},
+  abstract  = {It has been considered a fact that GPS performs too poorly inside buildings to provide usable indoor positioning. We analyze results of a measurement campaign to improve on the understanding of indoor GPS reception characteristics. The results show that using state-of-the-art receivers GPS availability is good in many buildings with standard material walls and roofs. The measured root mean squared 2D positioning error was below five meters in wooden buildings and below ten meters in most of the investigated brick and concrete buildings. Lower accuracies, where observed, can be linked to either low signal-to-noise ratios, multipath phenomena or bad satellite constellation geometry. We have also measured the indoor performance of embedded GPS receivers in mobile phones which provided lower availability and accuracy than state-of-the-art ones. Finally, we consider how the GPS performance within a given building is dependent on local properties like close-by building elements and materials, number of walls, number of overlaying stories and surrounding buildings.},
+  isbn      = {978-3-642-12654-3},
+  priority  = {prio2},
+}
+
+@Article{Molina_2018a,
+  author         = {Benjamin Molina and Eneko Olivares and Carlos Enrique Palau and Manuel Esteve},
+  journal        = {{IEEE} Access},
+  title          = {{A} {M}ultimodal {F}ingerprint-{B}ased {I}ndoor {P}ositioning {S}ystem for {A}irports},
+  year           = {2018},
+  pages          = {10092--10106},
+  volume         = {6},
+  doi            = {10.1109/access.2018.2798918},
+  publisher      = {Institute of Electrical and Electronics Engineers ({IEEE})},
+  qualityassured = {qualityAssured},
+}
+
+@Article{BluetoothSIG_2021,
+  author = {Bluetooth {SIG}, Inc},
+  title  = {2021 {M}arket {U}pdate},
+  year   = {2021},
+  file   = {:files/2021-Bluetooth_Market_Update.pdf:PDF},
+  url    = {https://www.bluetooth.com/wp-content/uploads/2021/01/2021-Bluetooth_Market_Update.pdf},
+}
+
+@Book{Grote_2007,
+  author    = {Karl-Heinrich Grote and Jörg Feldhusen},
+  editor    = {Karl-Heinrich Grote and Jörg Feldhusen},
+  publisher = {Springer Berlin Heidelberg},
+  title     = {Dubbel},
+  year      = {2007},
+  address   = {Berlin New York},
+  edition   = {22},
+  isbn      = {978-3-540-49714-1},
+  month     = {2007},
+  doi       = {10.1007/978-3-540-68191-5},
+}
+
+@Misc{BluetoothSIG_2021a,
+  author = {Bluetooth {SIG}, Inc.},
+  note   = {Abgerufen: 2021-11-29},
+  title  = {{B}luetooth {T}echnologie-{Ü}bersicht},
+  year   = {2021},
+  url    = {https://www.bluetooth.com/de/learn-about-bluetooth/tech-overview/},
+}
+
+@Book{Lerch_2006_BOOK,
+  author         = {Lerch, Reinhard},
+  publisher      = {Springer},
+  title          = {{E}lektrische {M}esstechnik},
+  year           = {2006},
+  address        = {Berlin Heidelberg},
+  edition        = {3},
+  isbn           = {9783540340553},
+  series         = {Springer-Lehrbuch},
+  doi            = {10.1007/3-540-34057-2},
+  file           = {:files/Lerch2006_Book_ElektrischeMesstechnik.pdf:PDF},
+  groups         = {Verwendet},
+  priority       = {prio2},
+  qualityassured = {qualityAssured},
+}
+
+@Misc{Duden_Distanze,
+  author    = {Dudenredaktion},
+  title     = {Duden - Die deutsche Rechtschreibung},
+  keywords  = {duden},
+  publisher = {Bibliographisches Institut},
+  url       = {https://www.duden.de/node/33536/revision/617769},
+}
+
+@Book{Strang_2008_BOOK,
+  author         = {Thomas Strang and Frank Schubert and Steffen Thölert and Rainer Oberweis},
+  publisher      = {Shaker},
+  title          = {Lokalisierungsverfahren},
+  year           = {2008},
+  address        = {Aachen},
+  isbn           = {9783832274924},
+  series         = {Geoinformatik},
+  comment        = {Deutsches Zentrum für Luft- und Raumfahrt, (DLR)
+
+URL: https://elib.dlr.de/54309/1/Lokalisierungsverfahren_v22.pdf},
+  file           = {:files/Lokalisierungsverfahren_v22.pdf:PDF},
+  groups         = {Verwendet},
+  keywords       = {GPS, Galileo, Indoor, Intertial, GBAS, SBAS, Ubisense, Kreisel, Navigation, Position},
+  priority       = {prio2},
+  qualityassured = {qualityAssured},
+}
+
 @Comment{jabref-meta: databaseType:bibtex;}
 
 @Comment{jabref-meta: grouping:
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 2 SearchGroup:Bluetooth\;0\;bluetooth\;0\;0\;1\;0x334db3ff\;bluetooth\;\;;
+2 SearchGroup:Wifi\;0\;Wifi\;0\;0\;1\;0x8a8a8aff\;\;\;;
 2 StaticGroup:Distanzmessung\;0\;1\;0x336666ff\;binoculars\;\;;
 2 StaticGroup:Methoden\;0\;1\;0xcc8033ff\;unicorn\;\;;
-3 SearchGroup:Filtering\;0\;filter\;0\;0\;1\;0x8a8a8aff\;\;\;;
+3 SearchGroup:Filtering\;0\;filter\;0\;0\;1\;0x8a8a8aff\;\;Filtermethoden und Anwendung von Filter\;;
+2 StaticGroup:Thesis von anderen\;0\;0\;0x8a8a8aff\;\;\;;
+2 StaticGroup:Verwendet\;0\;1\;0x30ff30ff\;\;\;;
 }