current TeX

switched to acm-large

tex/chapters/introduction.tex

@@ -1,7 +1,7 @@
 \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
+	(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
@@ -11,29 +11,29 @@
 	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,
+	Depending on the used 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
+	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,
+	towards it. While the provided accuracy is relatively low,
 	it can be stabilized by the IMU and vice versa.
 	
 	
-	The downside of such an approach: both sensors require additional prior
+	The downside of such an approach: both, \docWIFI{} and \docIBeacon{}s, 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 
+	location (prior knowledge). As RF-signals are highly dependent 
 	on the surroundings, those values can change rapidly within meters.
 	%
-	That is why fingerprinting became popular: The required prior knowledge 
+	That is why fingerprinting became popular, where 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
+	using cells with \SI{2}{\meter} in size. 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
@@ -48,35 +48,39 @@
 	%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.
+	Furthermore, the choice of the model's parameters depends on the actual architecture and \docWIFI{} setup:
+	Parameters that work within building A might not work out within building B.
 	
-	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.
+	Thus, a compromise comes to mind: Instead of using hundreds of fingerprints,
+	a few reference measurements used for a model setup might be a valid tradeoff
+	between resulting accuracy and necessary setup time.
 	
 	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.
+	for real-time applications on commodity smartphones.
+	%
+	To mitigate the issues of those signal strength predictors, we propose a new model
+	that is a combination of several simple ones. It is more accurate, requires only minor
+	additional computations and thus is well suited for use in mobile applications.
+	%
+	The to-be-expected accuracy (in \decibel{} and \meter{}) of all 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.
 	
 	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.
+	a closer look at the resulting performance-changes within a fully featured smartphone-based
+	indoor localization system using a movement model based on the building's floorplan,
+	together with various other sensors and recursive state estimation based on a particle filter.
 
-	\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
+	%}