introduction

tex/chapters/conclusion.tex

@@ -1,17 +1,22 @@
-conclusion
+\section{Conclusion}
 
-beide ansaetze sind in unserem szenario/gebaeude OK:
-bekannte AP-pos + empirische parameter
-komplette optimierung über fingerprints
+	beide ansaetze sind in unserem szenario/gebaeude OK:
+	bekannte AP-pos + empirische parameter
+	komplette optimierung über fingerprints
 
-100 prozent optimierung ist nicht moeglich, es gibt
-immer stellen, die, zugunsten von anderen, schlechter werden.
-es haengt auch stark davon ab, was man optimiert, das modell,
-die uebereinstimmung, welche fingerprints [schlechte vs. gute stellen]
+	100 prozent optimierung ist nicht moeglich, es gibt
+	immer stellen, die, zugunsten von anderen, schlechter werden.
+	es haengt auch stark davon ab, was man optimiert, das modell,
+	die uebereinstimmung, welche fingerprints [schlechte vs. gute stellen]
 
-zudem ist das modell fuer unser gebaeude nicht gut ggeeignet.
-zu viele verschiedene materialien und trennwaende, APs immer in raeumen,
-nie auf dem flur. viele hindernisse, wenige freie raeume.
-andere modelle koennten hier helfen, erfordern dann aber zur
-laufzeit mehr berechnung, oder muessten vorab auf einem grid berechnet
-werden \todo{cite auf competition}
+	zudem ist das modell fuer unser gebaeude nicht gut ggeeignet.
+	zu viele verschiedene materialien und trennwaende, APs immer in raeumen,
+	nie auf dem flur. viele hindernisse, wenige freie raeume.
+	andere modelle koennten hier helfen, erfordern dann aber zur
+	laufzeit mehr berechnung, oder muessten vorab auf einem grid berechnet
+	werden \todo{cite auf competition}
+
+\section{Future Work}
+
+	Komplexere Modelle die vorab berechnet werden und dann einfach in einer
+	Datenstruktur abgelegt sind, die z.B. interpolation erlaubt etc.

tex/chapters/introduction.tex

@@ -1,34 +1,82 @@
-introduction
+\section{Introduction}
+
+	State of the art indoor localization systems use a fusion of multiple
+	(Smartphone) sensors to infer the pedestrian's current location within a building
+	based on a variety of sensor observations.
+	%
+	Among those, the internal IMU, namely accelerometer and gyroscope, is often
+	used as a core component, that provides accurate relative movement information
+	like step- and turn-detection. However, this requires the pedestrian's
+	initial position to be well known, e.g. using a GPS-fix just before
+	entering the building. Additionally, the sensor's error will sum up over
+	time.
+
+	Depending on the used sensor fusion method, the latter can be addressed,
+	using a movement model for the pedestrian, that prevents unlikely movements
+	and locations. However, this will obviously work only to some extent and still
+	requires the initial position to be at least vaguely known.
+	%
+	Thus, indoor localization systems incorporate the knowledge of sensors,
+	that provide absolute location information like \docWIFI{} and
+	\docIBeacon{}s. The signal strength of nearby transmitters, received
+	by the smartphone, yields a vague information about the distance
+	to each transmitter. While the provided accuracy is relatively low,
+	it can be stabilized by the IMU and vice versa.
 	
 	
-setupzeiten von indoor systemen sind hoch [fingerprinting]
-auch re-calibration kostet oft zeit
+	The downside of such an approach: both sensors require additional prior
+	knowledge to work: To infer the probability of the pedestrian currently
+	residing at an arbitrary location, one compares the signal strengths received
+	by the smartphone with the signal strengths one should receive at this
+	location (prior knowledge). As \docWIFI{} signals are highly dependent 
+	on the surroundings, those values can change rapidly within meters.
+	%
+	That is why fingerprinting became popular: The required prior knowledge 
+	is gathered by manually scanning each location within the building e.g.
+	using cells with size of \SI{2}{\meter}. While this provides the highest
+	possible accuracy due to actual measurements of the real situation,
+	one can easily realize the necessary amount of work for both, the initial
+	setup and maintenance when transmitters are changed or renovations take
+	place.
 	
-meistens hat man einen gebäudeplan
-oft auch die info wo APs hängen
-warum das nicht nutzen und mit einer groben AP position
-+ fixen, empirischen param starten?
+	To prevent setup- and maintenance effort, models can be used to predict
+	the signal strengths one should receive at some arbitrary location.
+	Depending on the used model, only a few parameters and the location of the 
+	transmitter within the building are required. For newer installations 
+	the latter is often available and tagged within the building's floorplan.
+	%As signals are attenuated by the buildings architecture like walls and floors,
+	%advanced models additionally include the floorplan within their prediction.
+	Obviously, simple models will represent the real signal strengths only
+	to some extent, as not all ambient conditions, such as walls, are considered.
+	Furthermore, the choice of the model's parameters depends on the actual setup
+	and parameters that work within building A might not work out within building B.
 	
-was bekomme ich für eine genauigkeit raus?
+	Thus, a compromise comes to mind, that a few reference measurements used
+	for a viable model setup might be a valid tradeoff between accuracy and
+	setup time.
 	
-was kann ich machen um das zu verbessern?
-model parameter anlernen?
+	Within this work we will focus on simple signal strength prediction models
+	that do not incorporate knowledge of nearby walls, but can be used
+	for real-time applications on commodity smartphones. The to-be-expected accuracy
+	of those models is analyzed for various setups ranging from just empirical
+	parameters (no setup time when transmitter positions are known) to optimized
+	parameters where no prior knowledge is necessary and a few reference measurements
+	suffice.
 	
-wo sind die schwächen?
-verschiedene modelle mit unterschiedlichem berechnungsaufwand.
+	Despite analyzing the \docWIFI{} performance on its own, we will also have
+	a closer look at the to-be-expected performance within a complete indoor 
+	localization setup using a floorplan-based movement model together with
+	various sensors via recursive state estimation based on a particle filter.
 
-indoor komplett-system mit IMU, abs-heading, rel-heading, wifi sensor
-gebäudeplan, bewegungsmodell
+	\todo{
+	fokus:\\
+	- wlan parameter + optimierung\\
+	- evaluation der einzel und gesamtergebnisse
+	}
 
-\todo{
-fokus:\\
-- wlan parameter + optimierung\\
-- evaluation der einzel und gesamtergebnisse
-}
-
-\todo{
-contribution:\\
-- neues wifi modell,\\
-- neues resampling,\\
-- model param optimierung + eval was es bringt
-}
+	\todo{
+	contribution?:\\
+	- neues wifi modell,\\
+	- neues resampling,\\
+	- model param optimierung + eval was es bringt
+	}

tex/chapters/relatedwork.tex

@@ -1,3 +1,5 @@
 relatedwork
 
+wifi anfänge von radar (microsoft) etc
+
 \cite{Ebner-15}