code that runs the final eval

TeX update

CMakeLists.txt

@@ -44,6 +44,10 @@ FILE(GLOB SOURCES
 )
 
 
+# use openMP
+find_package(OpenMP REQUIRED)
+set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fopenmp")
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fopenmp")
 
 # system specific compiler flags
 ADD_DEFINITIONS(
@@ -58,7 +62,7 @@ ADD_DEFINITIONS(
 	-fstack-protector-all
 
 	-g3
-	-O2
+	-O0
 	-march=native
 
 	-DWITH_TESTS

Settings.h

@@ -57,7 +57,7 @@ namespace Settings {
 
 	const std::string fPathGFX = pathbase + "/tex/gfx/";
 
-	int numParticles = 5000;
+	int numParticles = 10000;
 
 	float smartphoneAboveGround = 1.3;
 

main.cpp

@@ -987,6 +987,9 @@ int main(void) {
 		//eval.show(Settings::path_toni_inst_2b, Settings::GroundTruth::path_toni_inst_2);
 		eval.show(Settings::path_toni_inst_3a, Settings::GroundTruth::path_toni_inst_3);
 
+
+
+
 		int i = 0; (void) i;
 
 	}
@@ -996,7 +999,9 @@ int main(void) {
 		Floorplan::IndoorMap* map = Floorplan::Reader::readFromFile(Settings::fMap);;
 		//EvalWalk walk(map);
 		//walk.walk1();
-		EvalWalk::walkEverything(map);
+
+		//75% quartil > 20 meter -> fehler? [verlaufen irgendwo ab der hälfte]
+		//EvalWalk::walkEverything(map);
 		EvalWalk::walkEverythingBuildStats(map);
 		return 0;
 	}

pf/EvalWalk.h

@@ -45,7 +45,7 @@
 #include <Indoor/sensors/offline/Listener.h>
 
 
-#define PLOT_LIVE
+//#define PLOT_LIVE
 //#define PLOT_WIFI
 //#define PLOT_ERROR_TIME
 //#define PLOT_ERROR_FUNC
@@ -60,10 +60,21 @@ class EvalWalk : public Offline::Listener {
 
 	std::string runName;
 
+#ifdef PLOT_LIVE
 	Plotty plotty;
+#endif
+
+#ifdef PLOT_WIFI
 	PlotWifiMeasurements plotWifi;
+#endif
+
+#ifdef PLOT_ERROR_TIME
 	PlotErrTime pet;
+#endif
+
+#ifdef PLOT_ERROR_FUNC
 	PlotErrFunc pef;
+#endif
 
 	Offline::FileReader reader;
 	Offline::FilePlayer player;
@@ -91,15 +102,34 @@ class EvalWalk : public Offline::Listener {
 
 public:
 
-	EvalWalk(Floorplan::IndoorMap* map) : plotty(map), map(map), em(map),
+	~EvalWalk() {
-		pet("\\small{time (sec)}", "\\small{error (m)}", "\\small{APs visible}"),
+		delete grid;
-		pef("\\small{error (m)}", "\\small{updates (\\%)}") {
+		delete pf;
+	}
+
+	EvalWalk(Floorplan::IndoorMap* map) :
+
+	#ifdef PLOT_LIVE
+		plotty(map),
+	#endif
+
+		map(map), em(map)
+
+	#ifdef PLOT_ERROR_TIME
+		,pet("\\small{time (sec)}", "\\small{error (m)}", "\\small{APs visible}")
+	#endif
+	#ifdef PLOT_ERROR_FUNC
+		,pef("\\small{error (m)}", "\\small{updates (\\%)}")
+	#endif
+	{
 
 		const std::string saveFile = Settings::pathData + "/grid.dat";
 		grid = new Grid<MyGridNode>(Settings::Grid::gridSize_cm);
 
-		// once
+		#ifdef PLOT_LIVE
-		plotty.buildFloorplan();
+			// once
+			plotty.buildFloorplan();
+		#endif
 
 		// deserialize grid
 		std::ifstream inp(saveFile, std::ofstream::binary);
@@ -173,12 +203,13 @@ public:
 
 	struct LeModel {
 		std::string name;
+		std::string plotKey;
 		WiFiModel* model;
-		LeModel(const std::string& name, WiFiModel* model) : name(name), model(model) {;}
+		LeModel(const std::string& name, const std::string& plotKey, WiFiModel* model) : name(name), plotKey(plotKey), model(model) {;}
 	};
 
 	static void serialize(const K::Statistics<float>& stats, const LeWalk& walk, const LeModel& model, const int idx) {
-		const std::string file = Settings::fPathGFX + "/walks/data/" + walk.name + "_" + model.name + "_" + std::to_string(idx) + ".txt";
+		const std::string file = Settings::fPathGFX + "/walks/data/" + model.name + "/" + walk.name + "_" + model.name + "_" + std::to_string(idx) + ".txt";
 		std::ofstream out(file);
 		for (const float f : stats.getAll()) {
 			out << f << "\n";
@@ -187,7 +218,7 @@ public:
 	}
 
 	static K::Statistics<float>* deserialize(const LeWalk& walk, const LeModel& model, const int idx) {
-		const std::string file = Settings::fPathGFX + "/walks/data/" + walk.name + "_" + model.name + "_" + std::to_string(idx) + ".txt";
+		const std::string file = Settings::fPathGFX + "/walks/data/" + model.name + "/" + walk.name + "_" + model.name + "_" + std::to_string(idx) + ".txt";
 		K::Statistics<float>* res = new K::Statistics<float>();
 		std::ifstream inp(file);
 		while(inp) {
@@ -221,16 +252,16 @@ public:
 	static std::vector<LeModel> allModels(Floorplan::IndoorMap* map) {
 		WiFiModelFactory fac(map);
 		return {
-			//LeModel("empiric", fac.loadXML(Settings::wifiAllFixed)),
+			LeModel("empiric", "\\noOptEmpiric{}", fac.loadXML(Settings::wifiAllFixed)),
-			//LeModel("opt 1", fac.loadXML(Settings::wifiAllOptPar)),
+			LeModel("opt 1", "\\optParamsAllAP{}", fac.loadXML(Settings::wifiAllOptPar)),
-			//LeModel("opt 2", fac.loadXML(Settings::wifiEachOptPar)),
+			LeModel("opt 2", "\\optParamsEachAP{}", fac.loadXML(Settings::wifiEachOptPar)),
-			//LeModel("opt 3", fac.loadXML(Settings::wifiEachOptParPos)),
+			LeModel("opt 3", "\\optParamsPosEachAP{}", fac.loadXML(Settings::wifiEachOptParPos)),
-			//LeModel("per floor", fac.loadXML(Settings::wifiEachOptParPos_multimodel)),
+			LeModel("per floor", "\\optPerFloor{}", fac.loadXML(Settings::wifiEachOptParPos_multimodel)),
-			LeModel("per bbox", fac.loadXML(Settings::wifiEachOptParPos_perBBox)),
+			LeModel("per bbox", "\\optPerRegion{}", fac.loadXML(Settings::wifiEachOptParPos_perBBox)),
 		};
 	}
 
-	static constexpr int numRepeats = 1;
+	static constexpr int numRepeats = 25;
 
 	/** create GFX for all previously walked parts */
 	static void walkEverythingBuildStats(Floorplan::IndoorMap* map) {
@@ -239,8 +270,10 @@ public:
 		const std::vector<LeModel> models = allModels(map);
 
 		PlotErrFunc* pefAll = new PlotErrFunc();
-		int numStuck = 0;
+		pefAll->setYRange(0, 90, 5);
-		int numTotal = 0;
+		pefAll->getPlot().getAxisY().setRange(0, 90);
+		pefAll->getPlot().getAxisY().setTicsStep({0, 25, 50, 75, 90});
+		pefAll->getPlot().getAxisX().setRange(0, 15);
 
 		for (const LeModel& mdl : models) {
 
@@ -248,34 +281,71 @@ public:
 			K::Statistics<float>* modelStatsMed = new K::Statistics<float>();
 
 			K::Statistics<float>* modelStatsSingle = new K::Statistics<float>();
-			pefAll->add(mdl.name, modelStatsSingle);
+			pefAll->add(mdl.plotKey, modelStatsSingle);
 
 
+			int numStuck = 0;
+			int numTotal = 0;
+
+			std::cout << "model " << mdl.name << std::endl;
+
 
 			// ... to perform every walk...
 			for (const LeWalk& walk : walks) {
 
-				for (int i = 0; i < numRepeats; ++i) {
+				int numStuckWalk = 0;
+				int numTotalWalk = 0;
+
+				// stats for all runs of one walk
+				K::Statistics<float> walkStatsAvg;
+
+				for (int i = 0; i < 75; ++i) {
 
 					++numTotal;
+					++numTotalWalk;
+
 					K::Statistics<float>* walkStats = deserialize(walk, mdl, i);
 
-					if (walkStats->getMedian() < 10) {
+					//if (walkStats->getMedian() < 15) {
-						modelStatsSingle->add(*walkStats);
+					if (walkStats->getQuantile(0.75) < 20) {
-						pefAll->plot();
+						//modelStatsSingle->add(*walkStats);
 					} else {
 						++numStuck;
+						++numStuckWalk;
 					}
 
+					modelStatsSingle->add(*walkStats);
+					walkStatsAvg.add(*walkStats);
+
 				}
 
+				if (modelStatsSingle->getCount() > 0) {
+					pefAll->plot();
+				}
+
+				std::cout << "|" << walk.name << "(" << walkStatsAvg.getMedian() << "@" << (numStuckWalk*100/numTotalWalk) << "%)\t";
+
 			}
 
+			std::cout << " | OVERALL:" << "(" << modelStatsSingle->getMedian() << "@" << (numStuck*100/numTotal) << "%)" << std::endl;
 			sleep(1);
 
 		}
 
-		std::cout << "stuck: " << numStuck << " (" << (numStuck*100/numTotal) << "%)" << std::endl;
+		pefAll->getGP().setTerminal("epslatex", K::GnuplotSize(8.6, 3.7));
+		pefAll->getGP().setOutput(Settings::fPathGFX + "/overall-system-error.tex");
+		pefAll->writePlotToFile(Settings::fPathGFX + "/overall-system-error.gp");
+		pefAll->getPlot().getMargin().set(4, 0.2, 0.1, 1.8);
+		pefAll->getPlot().setStringMod(new K::GnuplotStringModLaTeX());
+		pefAll->getPlot().getKey().setVisible(true);
+		pefAll->getPlot().getKey().setPosition(K::GnuplotKey::Hor::RIGHT, K::GnuplotKey::Ver::BOTTOM);
+		pefAll->getPlot().getKey().setWidthIncrement(7);
+		pefAll->getPlot().getAxisX().setTicsLabelFormat("%h m");
+		pefAll->getPlot().getAxisX().setRange(0, 22);
+		pefAll->getPlot().getAxisX().setLabel("");
+		pefAll->getPlot().getAxisY().setLabel("filter updates (%)");
+		pefAll->getPlot().getAxisY().setLabelOffset(3.0, 0);
+		pefAll->plot();
 
 		sleep(100);
 
@@ -303,7 +373,8 @@ public:
 				//K::Statistics<float> curWalkRepeatStatsMed;
 
 				// ... several times
-				for (int i = 0; i < numRepeats; ++i) {
+				//#pragma omp parallel for num_threads(2)
+				for (int i = numRepeats*2; i < numRepeats*3; ++i) {
 
 					EvalWalk ew(map);
 					ew.walkName = walk.name + " - " + mdl.name + " - " + std::to_string(i);
@@ -530,6 +601,7 @@ private:
 		std::string path = Settings::fPathGFX;
 		std::string base = path + "/walks/" + "walk-" + walkName;
 
+#ifdef PLOT_LIVE
 		plotty.gp << "unset arrow 1\n";
 		plotty.gp << "unset arrow 2\n";
 		plotty.gp << "unset colorbox\n";
@@ -546,18 +618,23 @@ private:
 		plotty.gp.setOutput(base + "-map.tex");
 		plotty.writeCodeTo(base + "-map.gp");
 		plotty.plot();
+#endif
 
+#ifdef PLOT_ERROR_FUNC
 		pef.getPlot().getMargin().set(4, 0.2, 0.1, 2.0);
 		pef.getGP().setTerminal("epslatex", K::GnuplotSize(8.6, 3.0));
 		pef.getGP().setOutput(base + "-error-cum.tex");
 		pef.writePlotToFile(base + "-error-cum.gp");
 		pef.plot();
+#endif
 
+#ifdef PLOT_ERROR_TIME
 		pet.getPlot().getMargin().set(4, 0.2, 0.1, 2.0);
 		pet.getGP().setTerminal("epslatex", K::GnuplotSize(8.6, 3.0));
 		pet.getGP().setOutput(base + "-error-time.tex");
 		pet.writePlotToFile(base + "-error-time.gp");
 		pet.plot();
+#endif
 
 	}
 
@@ -566,10 +643,6 @@ private:
 
 		++updateCount;
 
-		pef.showMarkers(true, true);
-
-		std::cout << "update" << std::endl;
-
 		MyControl ctrlCopy = curCtrl;
 		//static float absHead = relHeadingOffset;
 		absHead += ctrlCopy.turnSinceLastTransition_rad;
@@ -587,6 +660,7 @@ private:
 			statsErr.add(estRealErr);
 
 #ifdef PLOT_ERROR_FUNC
+			pef.showMarkers(true, true);
 			pef.clear();
 			pef.add("", &statsErr);
 #endif
@@ -597,9 +671,11 @@ private:
 			pet.addB(lastTransition, getNumFHWSAPs(curObs.wifi));
 #endif
 
+#ifdef PLOT_LIVE
 			// update estimated path
 			const K::GnuplotPoint3 p3(curEst.position.x_cm, curEst.position.y_cm, curEst.position.z_cm);
 			plotty.pathEst.add(p3/100);
+#endif
 
 		}
 
@@ -611,6 +687,7 @@ private:
 
 			std::cout << statsErr.asString() << std::endl;
 
+#ifdef PLOT_LIVE
 			// estimation and ground-truth
 			plotty.setCurEst(curEst.position.inMeter());
 			plotty.setGroundTruth(curGT);
@@ -654,6 +731,7 @@ private:
 				plotty.gp << "set arrow 2 from screen " << cen.x << "," << cen.y << " to screen " << arr.x << "," << arr.y << "\n";
 			}
 
+#endif
 
 #ifdef PLOT_LIVE
 			plotty.plot();	

pf/PF.h

@@ -373,7 +373,7 @@ public:
 
 		wqa.add(wifiObs);
 		float quality = wqa.getQuality();
-		std::cout << "wifi quality: " << quality << std::endl;
+		//std::cout << "wifi quality: " << quality << std::endl;
 
 
 		// GPS

tex/bare_conf.tex

@@ -139,7 +139,7 @@
 % not capitalized unless they are the first or last word of the title.
 % Linebreaks \\ can be used within to get better formatting as desired.
 % Do not put math or special symbols in the title.
-\title{minimizing system setup times }
+\title{\docWIFI{} Optimization for Smartphone-based Indoor Localization}
 
 
 % author names and affiliations

tex/chapters/conclusion.tex

@@ -1,22 +1,57 @@
-\section{Conclusion}
+\section{Conclusion and Future Work}
 
-	beide ansaetze sind in unserem szenario/gebaeude OK:
+	\todo{ueberleitung?}
-	bekannte AP-pos + empirische parameter
-	komplette optimierung über fingerprints
 
-	100 prozent optimierung ist nicht moeglich, es gibt
+	As denoted within the previous evaluations and discussions, the accuracy of 
-	immer stellen, die, zugunsten von anderen, schlechter werden.
+	indoor localization systems based on \docWIFI{} depends on a manifold
-	es haengt auch stark davon ab, was man optimiert, das modell,
+	of parameters and even minor adjustments can yield huge improvements.
-	die uebereinstimmung, welche fingerprints [schlechte vs. gute stellen]
+	Depending on required accuracy and acceptable setup- and maintenance times,
+	several approaches are conceivable:
 
-	zudem ist das modell fuer unser gebaeude nicht gut ggeeignet.
+	If the use-case does not require utmost accuracy and the locations of permanently
-	zu viele verschiedene materialien und trennwaende, APs immer in raeumen,
+	installed transmitters is already well known, just using empiric model parameters
-	nie auf dem flur. viele hindernisse, wenige freie raeume.
+	is a viable choice for many situations.
-	andere modelle koennten hier helfen, erfordern dann aber zur
+	
-	laufzeit mehr berechnung, oder muessten vorab auf einem grid berechnet
+	However, when combined with (particle) filtering, a heavily constrained 
-	werden \todo{cite auf competition}
+	movement model might be a potential issue, as it might get stuck when
+	sensor observations or model predictions are too erroneous.
+	
+	Using a small number of reference measurements will already suffice
+	to improve such errors. Furthermore it also removes the need for prior knowledge
+	like transmitter locations, as those parameters can be estimated via optimization.
+	
+	For the best accuracy, more complex signal strength propagation models
+	are required which, in turn, demand for more reference measurements.
+	%
+	However, while using a several instances of a simple propagation model
+	for different regions within a building is able to decrease the estimation 
+	error, this approach might require prior guessing of where to place those regions
+	and is still unable to approximate all signal strength variations within the building.
+	
+	More complex models that include information about walls and other obstacles should 
+	be able to improve the situation at the cost of additional computation.
+	Special data-structures for pre-computation combined with online interpolation might
+	be a viable choice for utmost accuracy while still being able to run on
+	a commodity smartphone in realtime.
+	
+	While we were able to improve the performance of the \docWIFI{} sensor component,
+	the filtering process should be more robust against erroneous observations.
+	Getting stuck should be prevented, independent of minor changes in quality for
+	the signal strength prediction model \cite{todo-toni}.
+	
+	
+	%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]
 
-\section{Future Work}
+	%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
+	%aufzeit mehr berechnung, oder muessten vorab auf einem grid berechnet
+	%werden \todo{cite auf competition}
+
+
+	%besseres modell mit (leichter) interpolation zwischen den randbereichen?
 
-	Komplexere Modelle die vorab berechnet werden und dann einfach in einer
-	Datenstruktur abgelegt sind, die z.B. interpolation erlaubt etc.

tex/chapters/experiments.tex

@@ -1,21 +1,26 @@
 \section{Experiments}
 
-	\todo{
+	% intro
-		alles im FHWS gebäude [korrekte groesse fuer beide gebaeude!] mit nem nexus 6
-	}
 	
-	Within all \docWIFI{} observations (offline and online) we only consider the \docAP{}s that are permanently installed
+	All optimizations and evaluations took place within two adjacent buildings (4 and 2 floors, respectively)
-	within the building. Temporal and movable transmitters are ignored as they might cause estimation errors.
+	and two connected outdoor regions (entrance and inner courtyard),
+	yielding a total size of \SI{110}{\meter} x \SI{60}{\meter}.
+	
+	Within all \docWIFI{} observations we only consider the \docAP{}s that are permanently installed,
+	and can be identified by their well-known MAC address.
+	Temporal and movable transmitters are ignored as they might cause estimation errors.
+	
+	Unfortunately, due to non-disclosure agreements, we are not allowed to depict the actual location
+	of installed transmitters within the following figures. 
 	
 	
+	%modell direkt fuer den gelaufenen pfad optimiert (also wirklich jede wifi messung direkt auf den ground-truth)
+	%der fehler wird zwar kleiner, ist aber immernoch deutlich spürbar. das spricht dafür, dass das modell einfach nicht
+	%gut geeignet ist.
 
-	modell direkt fuer den gelaufenen pfad optimiert (also wirklich jede wifi messung direkt auf den ground-truth)
+	%optimierungs input: alle 4 walks samt ground-truth
-	der fehler wird zwar kleiner, ist aber immernoch deutlich spürbar. das spricht dafür, dass das modell einfach nicht
+	%dann kommt fuer die 4 typen [fixed, all same par, each par, each par pos]
-	gut geeignet ist.
+	%log probability 50 75,	meter 50, 75
-
-	optimierungs input: alle 4 walks samt ground-truth
-	dann kommt fuer die 4 typen [fixed, all same par, each par, each par pos]
-	log probability 50 75,	meter 50, 75
 
 
 
@@ -214,7 +219,7 @@
 	
 	\subsection{Location estimation error}
 	
-		\todo{übergang holprig}
+		\todo{uebergang holprig?}
 	
 		%Using the optimized model setups and the measurements $\mRssiVec$ determined by scanning for nearby \docAPshort{}s,
 		%we can directly perform a location estimation by rewriting \refeq{eq:wifiProb}:
@@ -251,7 +256,7 @@
 		
 		
 		
-		We therefore conducted 10 walks on 5 different paths within our building,
+		We therefore conducted 13 walks on 5 different paths within our building,
 		each of which is defined by connecting marker points at well known positions
 		(see figure \ref{fig:allWalks}).
 		Whenever the pedestrian reached such a marker, the current time was recorded.
@@ -322,7 +327,7 @@
 		
 		To reduce the amount such of misclassifications, where other locations within the building are 
 		as likely as the pedestrians actual location, we examined various approaches.
-		Unfortunately, none of which provided a viable enhancement under all conditions for the performed walks.
+		Unfortunately, most of which did not provided a viable enhancement under all conditions for the performed walks.
 		
 		The misclassification-rate is determined by counting the amount of (random) locations within
 		the building that produce a similar probability \refeq{eq:wifiProb} as the actual ground-truth
@@ -347,12 +352,11 @@
 		The models used within this work will not accurately predict the signal strength for such locations.
 		%%Including \docAPshort{}s unseen by the Smartphone thus often increases the estimation error instead
 		%%of fixing the multimodality.
-		
+				
 		To sum up, while some situations, e.g. outdoors, could greatly be improved, 
 		many other situations are deteriorated, especially when some transmitters are (temporarily)
 		attenuated by ambient conditions like concrete walls.
-		
+				
-		
 		
 		We therefore examined variations of the probability calculation from \refeq{eq:wifiProb}.
 		Removing the strongest/weakest \docAPshort{} from $\mRssiVec{}$ yielded similar results.
@@ -360,105 +364,203 @@
 		as there are many situations where only a handful \docAP{}s can be seen. Removing (valid)
 		information will highly increase the error for such situations.
 		
-		Using a more strict exponential distribution for the model vs. scan comparison in \refeq{eq:wifiProb}
+		Incorporating additional knowledge provided by virtual \docAP{}s (see section \ref{sec:vap}) mitigated this issues.
-		had a positive effect on the misclassification error for some of the walks, but slightly increased 
+		If only one out of six virtual networks is observed, this observation is likely to be erroneous, no matter
-		the estimation error (see figure \ref{fig:normalVsExponential}) and thus produced negative side effects.
+		what the corresponding signal strength indicates. This approach improved the location estimation especially
+		for areas where a transmitter was hardly seen within the reference measurements and its optimization is thus
+		expected to be inaccurate.		
 		
-		\begin{figure}
+		Using a smaller $\sigma$ or a more strict exponential distribution for the model vs. scan comparison in \refeq{eq:wifiProb}
-			\input{gfx/wifiCompare_normalVsExp_cross.tex}
+		had a positive effect on the misclassification error for some of the walks, but also slightly increased the overall estimation error.
-			\input{gfx/wifiCompare_normalVsExp_meter.tex}
+		%(see figure \ref{fig:normalVsExponential}).
-			\caption{
+		Due to those negative side-effects, the final localization system (\refeq{eq:recursiveDensity}) is unlikely to profit from such changes.
-				Comparison between normal- (black) and exponential-distribution (red) for \refeq{eq:wifiProb}.
+		
-				While misclassifications are slightly reduced (upper chart),
+		\todo{ueberleitung OK?}
-				the median error between ground-truth and estimation (lower chart) increases by
+		
-				about \SI{1}{\meter}.		 		
+		% braucht zu viel platz
-			}
+		%\begin{figure}
-			\label{fig:normalVsExponential}
+		%	\input{gfx/wifiCompare_normalVsExp_cross.tex}
-		\end{figure}
+		%	\input{gfx/wifiCompare_normalVsExp_meter.tex}
+		%	\caption{
+		%		Comparison between normal- (black) and exponential-distribution (red) for \refeq{eq:wifiProb}.
+		%		While misclassifications are slightly reduced (upper chart),
+		%		the median error between ground-truth and estimation (lower chart) increases by
+		%		about \SI{1}{\meter}.		 		
+		%	}
+		%	\label{fig:normalVsExponential}
+		%\end{figure}
 		
 
-	
 
-	\todo{
+	%\todo{
-		erwähnen??? sigma je nach signalstärke anpassen bringt leider auch nichts. wenn man das aber macht,
+	%	erwähnen??? sigma je nach signalstärke anpassen bringt leider auch nichts. wenn man das aber macht,
-		dann: fuer grosse signalstaerken ein grosses sigma! andersrum gehts nach hinten los!
+	%	dann: fuer grosse signalstaerken ein grosses sigma! andersrum gehts nach hinten los!
-	}
+	%}
 
 
 
 
 	% -------------------------------- final system -------------------------------- %
 
+	\subsection{Overall system error}
+
+		After examining the \docWIFI{} component on its own, we will now analyze the impact of aforementioned model
+		optimizations on our smartphone-based indoor localization system described in section \ref{sec:system}.
+		
+		Due to other sensors and the transition constraints from the buildings floorplan, we expect the 
+		posterior density to often get stuck when the \docWIFI{} component provides erroneous estimations
+		due to bad signal strength predictions:
+		%
+		A pedestrian walks along a hallway, but bad model values indicate that his most likely position
+		is within a room right next to the hallway.
+		If the density (described by the particles) is dragged (completely) into this room, 
+		the IMU indicates no change in direction (pedestrian walks straight),
+		and the room has only one single door, the density is trapped within this room.
+		%
+		Such problems can often be solved by simply using more particles to describe the posterior.
+		
+		As particle filtering from \refeq{eq:recursiveDensity} is a random process with varying output,
+		we calculated each combination of the {\em 13 walks and optimization strategy},
+		25 times, using 5000, 7500 and 10000 particles resulting in 75 runs per walk, 975 per strategy and 5850 in total.
+		%
+		Figure \ref{fig:overallSystemError} depicts the error distribution per optimization strategy,
+		resulting from all executions for each walk conducted with the smartphone.
+		
+		While most values represent the expected results (more optimization yields better results),
+		the values for {\em \noOptEmpiric{}} and {\em \optPerRegion{}} do not.
+		The slightly increased error for both strategies can be explained by having a closer look at the walked
+		paths and relates to exceptional regions like outdoors. In both cases there is some sort of model overadaption.
+		%
+		As mentioned earlier, {\em \noOptEmpiric{}} is unable to accurately model the signal strength for the whole
+		building, resulting in increased estimation errors for outdoor regions, where the filter fails to conclude
+		the walk.
+		%
+		While {\em \optPerRegion{}} does not suffer from such issues due to separated optimization regions for in- and outdoor,
+		its increased error relates to movements between such adjacent regions, as there often is a huge model difference.
+		While this difference is perfectly fine, as it also exists within real world conditions,
+		the filtering process suffers especially at such model-boundaries:
+		The model prevents the particles from moving e.g. from inside the building towards outdoor regions, as the 
+		outdoor-model does not match at all. Due to sensor delays and issues with the absolute heading near in- and outdoor boundaries
+		(metal-framed doors) the error is slightly increased and retained for some time until the density stabilizes itself.
+		
+		Such situations should be mitigated by the smartphone's GPS sensor. However, within our testing walks, the GPS
+		did rarely provide accurate measurements, as the outdoor-time was to short for the sensor to receive a valid
+		fix. The accuracy indicated by the GPS usually was \SI{50}{\meter} and above.
+		
+		Especially for {\em path 1}, the particle-filter often got stuck within the upper right outdoor area between both buildings
+		(see figure \ref{fig:allWalks}). Using the empirical parameters, \SI{40}{\percent} of all runs for this path got stuck at this location.
+		While {\em \optParamsAllAP{}} already reduced the risk to \SI{20}{\percent}, all other optimization strategies did not get stuck at all.
+		
+		The same effect holds for all other conducted walks: The better the model optimization, the lower the risk of getting stuck somewhere along the path.
+		
+		Varying the number of particles between 5000 and 10000 indicated only a minor increase in accuracy and slightly decreased the risk of getting stuck.
+		
+		Comparing the results of figure \ref{fig:modelPerformance} and \ref{fig:overallSystemError} one can
+		denote the positive impact of fusioning multiple sensors with a transition model based on the buildings
+		actual floorplan. Especially the outdoor regions, or other areas with disabled \docWIFI{} component highly
+		profit from the data provided by the smartphones IMU, which prevents the estimation from getting lost.
+		
+		
+		\begin{figure}
+			\input{gfx/overall-system-error.tex}
+			\caption{
+				Cumulative error distribution for each model when used within the final localization system from \refeq{eq:recursiveDensity}.
+				Despite some discussed exceptions, highly optimized models lead to lower localization errors. 
+			}
+			\label{fig:overallSystemError}
+		\end{figure}
+
+	% results
+%	5000 particles
+%
+%		model empiric
+%		|path1a(5.76715@100%)	|path1b(3.73881@4%)	|toni-all-1a(6.1505@76%)	|toni-all-1b(4.60639@40%)	|path2a(7.35355@28%)	|path2b(7.4316@0%)	|toni-all-2a(10.7068@44%)	|toni-all-2b(7.4323@28%)	|toni-inst-1b(4.60685@0%)	|toni-inst-2a(3.83979@0%)	|toni-inst-2b(3.98889@0%)	|toni-inst-3a(4.70925@0%)	|toni-inst-3b(4.40971@0%)	 | OVERALL:(5.12463@24%)
+%		model opt 1
+%		|path1a(17.6635@56%)	|path1b(9.41882@24%)|toni-all-1a(4.06972@0%)	|toni-all-1b(3.83157@0%)	|path2a(6.92405@16%)	|path2b(8.6365@16%)	|toni-all-2a(11.6348@48%)	|toni-all-2b(12.029@76%)	|toni-inst-1b(5.07535@0%)	|toni-inst-2a(4.45517@0%)	|toni-inst-2b(3.99025@0%)	|toni-inst-3a(8.28201@8%)	|toni-inst-3b(5.57021@20%)	 | OVERALL:(6.57212@20%)
+%		model opt 2
+%		|path1a(2.01602@0%)		|path1b(2.90237@0%)	|toni-all-1a(2.80293@0%)	|toni-all-1b(1.99745@0%)	|path2a(5.39013@4%)		|path2b(8.13855@0%)	|toni-all-2a(9.7462@40%)	|toni-all-2b(9.28677@44%)	|toni-inst-1b(4.5305@0%)	|toni-inst-2a(4.28726@0%)	|toni-inst-2b(4.03041@0%)	|toni-inst-3a(4.26278@4%)	|toni-inst-3b(5.63394@24%)	 | OVERALL:(4.07822@8%)
+%		model opt 3
+%		|path1a(1.74623@0%)		|path1b(2.61609@0%)	|toni-all-1a(2.49372@0%)	|toni-all-1b(1.90326@0%)	|path2a(5.07957@4%)		|path2b(7.73973@8%)	|toni-all-2a(10.2793@48%)	|toni-all-2b(6.48194@16%)	|toni-inst-1b(5.73752@4%)	|toni-inst-2a(3.76165@0%)	|toni-inst-2b(3.51509@0%)	|toni-inst-3a(6.06681@16%)	|toni-inst-3b(5.27748@24%)	 | OVERALL:(3.94786@9%)
+%		model per floor
+%		|path1a(1.76139@0%)		|path1b(2.22047@0%)	|toni-all-1a(2.10094@0%)	|toni-all-1b(1.62287@0%)	|path2a(5.50715@16%)	|path2b(7.1257@0%)	|toni-all-2a(10.5138@48%)	|toni-all-2b(6.72044@20%)	|toni-inst-1b(3.77885@0%)	|toni-inst-2a(2.23669@0%)	|toni-inst-2b(3.20604@0%)	|toni-inst-3a(2.46891@0%)	|toni-inst-3b(2.73366@0%)	 | OVERALL:(3.22315@6%)
+%		model per bbox	
+%		|path1a(1.80033@0%)		|path1b(2.32875@0%)	|toni-all-1a(2.17754@0%)	|toni-all-1b(1.6697@0%)		|path2a(6.38772@16%)	|path2b(5.84004@0%)	|toni-all-2a(9.67635@36%)	|toni-all-2b(8.3282@24%)	|toni-inst-1b(4.11891@0%)	|toni-inst-2a(2.64016@0%)	|toni-inst-2b(3.36297@0%)	|toni-inst-3a(2.15568@0%)	|toni-inst-3b(2.98047@0%)	 | OVERALL:(3.40679@5%)
+%
+%
+%	7500 particles
+%		model empiric
+%		|path1a(8.23256@100%)	|path1b(3.91532@0%)	|toni-all-1a(7.0666@80%)	|toni-all-1b(5.35225@48%)	|path2a(6.5708@16%)		|path2b(7.53023@0%)	|toni-all-2a(10.6246@40%)	|toni-all-2b(6.63087@4%)	|toni-inst-1b(4.76934@0%)	|toni-inst-2a(3.82903@0%)	|toni-inst-2b(4.00339@0%)	|toni-inst-3a(3.85417@4%)	|toni-inst-3b(4.47613@0%)	 | OVERALL:(5.23337@22%)
+%		model opt 1
+%		|path1a(10.3959@36%)	|path1b(8.37674@16%)|toni-all-1a(3.96164@0%)	|toni-all-1b(4.24675@4%)	|path2a(6.02912@8%)		|path2b(8.1804@0%)	|toni-all-2a(12.4277@48%)	|toni-all-2b(10.4748@56%)	|toni-inst-1b(5.49874@4%)	|toni-inst-2a(4.09279@0%)	|toni-inst-2b(3.87762@0%)	|toni-inst-3a(5.10456@0%)	|toni-inst-3b(4.52029@4%)	 | OVERALL:(5.97832@13%)
+%		model opt 2
+%		|path1a(2.04657@0%)		|path1b(2.82853@0%)	|toni-all-1a(2.93467@0%)	|toni-all-1b(1.98463@0%)	|path2a(4.66513@8%)		|path2b(8.19959@0%)	|toni-all-2a(8.34246@12%)	|toni-all-2b(7.2456@12%)	|toni-inst-1b(4.72651@0%)	|toni-inst-2a(4.00208@0%)	|toni-inst-2b(3.94811@0%)	|toni-inst-3a(3.74498@0%)	|toni-inst-3b(5.15519@16%)	 | OVERALL:(3.99594@3%)
+%		model opt 3
+%		|path1a(1.82148@0%)		|path1b(2.7664@0%)	|toni-all-1a(2.46073@0%)	|toni-all-1b(1.93273@0%)	|path2a(5.15394@4%)		|path2b(7.53562@0%)	|toni-all-2a(8.43582@20%)	|toni-all-2b(6.01557@8%)	|toni-inst-1b(5.47576@0%)	|toni-inst-2a(3.44451@0%)	|toni-inst-2b(3.6069@0%)	|toni-inst-3a(4.84921@4%)	|toni-inst-3b(5.62456@8%)	 | OVERALL:(3.88747@3%)
+%		model per floor
+%		|path1a(1.79881@0%)		|path1b(2.1456@0%)	|toni-all-1a(2.17125@0%)	|toni-all-1b(1.63247@0%)	|path2a(5.37789@8%)		|path2b(6.79701@0%)	|toni-all-2a(9.29407@32%)	|toni-all-2b(6.28292@8%)	|toni-inst-1b(3.79967@0%)	|toni-inst-2a(2.24007@0%)	|toni-inst-2b(3.15768@0%)	|toni-inst-3a(2.17671@0%)	|toni-inst-3b(2.83445@0%)	 | OVERALL:(3.16559@3%)
+%		model per bbox
+%		|path1a(1.77473@0%)		|path1b(2.2609@0%)	|toni-all-1a(2.06814@4%)	|toni-all-1b(1.6841@0%)		|path2a(6.48652@4%)		|path2b(5.79359@0%)	|toni-all-2a(9.40116@24%)	|toni-all-2b(7.21382@16%)	|toni-inst-1b(3.82829@0%)	|toni-inst-2a(2.47975@0%)	|toni-inst-2b(3.35265@0%)	|toni-inst-3a(2.20058@0%)	|toni-inst-3b(2.86407@0%)	 | OVERALL:(3.3381@3%)
+%
+%	10000 particles
+%		model empiric
+%		|path1a(6.43082@100%)	|path1b(3.58544@0%)	|toni-all-1a(6.92747@76%)	|toni-all-1b(5.81139@72%)	|path2a(5.12683@4%)		|path2b(7.91078@0%)	|toni-all-2a(10.3958@16%)	|toni-all-2b(7.09186@8%)	|toni-inst-1b(4.45815@0%)	|toni-inst-2a(4.077@0%)		|toni-inst-2b(4.02524@0%)	|toni-inst-3a(3.35953@0%)	|toni-inst-3b(4.40318@0%)	 | OVERALL:(5.06224@21%)
+%		model opt 1
+%		|path1a(6.47262@16%)	|path1b(6.04852@12%)|toni-all-1a(3.97276@0%)	|toni-all-1b(3.62778@0%)	|path2a(5.48776@8%)		|path2b(8.21965@0%)	|toni-all-2a(11.3175@44%)	|toni-all-2b(11.4499@60%)	|toni-inst-1b(5.19827@0%)	|toni-inst-2a(4.1351@0%)	|toni-inst-2b(3.90291@0%)	|toni-inst-3a(4.58096@8%)	|toni-inst-3b(4.62723@4%)	 | OVERALL:(5.47998@11%)
+%		model opt 2
+%		|path1a(2.15007@0%)		|path1b(2.80157@0%)	|toni-all-1a(2.70849@0%)	|toni-all-1b(1.8937@0%)		|path2a(4.13743@0%)		|path2b(8.20317@0%)	|toni-all-2a(7.86448@12%)	|toni-all-2b(7.41533@12%)	|toni-inst-1b(4.54459@0%)	|toni-inst-2a(4.17614@0%)	|toni-inst-2b(3.90311@0%)	|toni-inst-3a(3.846@4%)		|toni-inst-3b(4.84665@8%)	 | OVERALL:(3.89883@2%)
+%		model opt 3
+%		|path1a(1.79085@0%)		|path1b(2.64892@0%)	|toni-all-1a(2.33085@0%)	|toni-all-1b(1.9533@0%)		|path2a(4.40712@4%)		|path2b(7.815@0%)	|toni-all-2a(8.97738@28%)	|toni-all-2b(5.87188@0%)	|toni-inst-1b(4.93315@0%)	|toni-inst-2a(3.53349@0%)	|toni-inst-2b(3.60056@0%)	|toni-inst-3a(5.57379@8%)	|toni-inst-3b(4.49996@4%)	 | OVERALL:(3.78756@3%)
+%		model per floor
+%		|path1a(1.7498@0%)		|path1b(2.11555@0%)	|toni-all-1a(1.89388@0%)	|toni-all-1b(1.61323@0%)	|path2a(5.06884@0%)		|path2b(6.7157@0%)	|toni-all-2a(9.54228@36%)	|toni-all-2b(6.7699@24%)	|toni-inst-1b(3.84709@0%)	|toni-inst-2a(2.2789@0%)	|toni-inst-2b(3.17625@0%)	|toni-inst-3a(2.13417@0%)	|toni-inst-3b(2.59095@0%)	 | OVERALL:(3.08506@4%)
+%		model per bbox
+%		|path1a(1.73406@0%)		|path1b(2.30577@0%)	|toni-all-1a(2.01979@0%)	|toni-all-1b(1.64225@0%)	|path2a(6.30713@12%)	|path2b(6.02961@0%)	|toni-all-2a(9.70206@20%)	|toni-all-2b(6.55847@8%)	|toni-inst-1b(3.93324@0%)	|toni-inst-2a(2.459@0%)		|toni-inst-2b(3.3522@0%)	|toni-inst-3a(2.13783@0%)	|toni-inst-3b(2.63231@0%)	 | OVERALL:(3.29408@3%)
+
+%	all combined
+%		model empiric
+%		|path1a(6.72661@100%)	|path1b(3.74113@1%)	|toni-all-1a(6.69696@77%)	|toni-all-1b(5.26661@53%)	|path2a(6.11286@16%)	|path2b(7.63154@0%)	|toni-all-2a(10.5765@33%)	|toni-all-2b(7.0506@13%)	|toni-inst-1b(4.61087@0%)	|toni-inst-2a(3.91375@0%)	|toni-inst-2b(4.00372@0%)	|toni-inst-3a(3.89586@1%)	|toni-inst-3b(4.43552@0%)	 | OVERALL:(5.13701@22%)
+%		model opt 1
+%		|path1a(10.0538@36%)	|path1b(7.96075@17%)|toni-all-1a(3.99762@0%)	|toni-all-1b(3.89137@1%)	|path2a(6.08714@10%)	|path2b(8.33165@5%)	|toni-all-2a(11.7481@46%)	|toni-all-2b(11.2068@64%)	|toni-inst-1b(5.25558@1%)	|toni-inst-2a(4.23255@0%)	|toni-inst-2b(3.92269@0%)	|toni-inst-3a(5.62327@5%)	|toni-inst-3b(4.82302@9%)	 | OVERALL:(6.00231@15%)
+%		model opt 2
+%		|path1a(2.07273@0%)		|path1b(2.84622@0%)	|toni-all-1a(2.81671@0%)	|toni-all-1b(1.9553@0%)		|path2a(4.66453@4%)		|path2b(8.17561@0%)	|toni-all-2a(8.60702@21%)	|toni-all-2b(7.68813@22%)	|toni-inst-1b(4.59132@0%)	|toni-inst-2a(4.15243@0%)	|toni-inst-2b(3.96315@0%)	|toni-inst-3a(3.96402@2%)	|toni-inst-3b(5.16219@16%)	 | OVERALL:(3.99259@5%)
+%		model opt 3
+%		|path1a(1.78819@0%)		|path1b(2.67775@0%)	|toni-all-1a(2.43527@0%)	|toni-all-1b(1.92948@0%)	|path2a(4.90009@4%)		|path2b(7.70505@2%)	|toni-all-2a(9.16313@32%)	|toni-all-2b(6.10436@8%)	|toni-inst-1b(5.37191@1%)	|toni-inst-2a(3.57332@0%)	|toni-inst-2b(3.57426@0%)	|toni-inst-3a(5.4337@9%)	|toni-inst-3b(5.12685@12%)	 | OVERALL:(3.86918@5%)
+%		model per floor
+%		|path1a(1.77029@0%)		|path1b(2.16265@0%)	|toni-all-1a(2.05043@0%)	|toni-all-1b(1.62289@0%)	|path2a(5.29536@8%)		|path2b(6.88344@0%)	|toni-all-2a(9.75416@38%)	|toni-all-2b(6.57473@17%)	|toni-inst-1b(3.80742@0%)	|toni-inst-2a(2.25183@0%)	|toni-inst-2b(3.18067@0%)	|toni-inst-3a(2.24992@0%)	|toni-inst-3b(2.72835@0%)	 | OVERALL:(3.15739@4%)
+%		model per bbox
+%		|path1a(1.76908@0%)		|path1b(2.30081@0%)	|toni-all-1a(2.09503@1%)	|toni-all-1b(1.66411@0%)	|path2a(6.39346@10%)	|path2b(5.8772@0%)	|toni-all-2a(9.59953@26%)	|toni-all-2b(7.06924@16%)	|toni-inst-1b(3.96094@0%)	|toni-inst-2a(2.51694@0%)	|toni-inst-2b(3.3549@0%)	|toni-inst-3a(2.1656@0%)	|toni-inst-3b(2.81547@0%)	 | OVERALL:(3.34847@4%)
+
+
 	% REAL WALKS
-		\todo{obwohl das angepasste modell doch recht gut laeuft und der fehler recht klein wird, sind immernoch stellen dabei,
+	
-		wo es einfach nicht gut passt, unguenstige mehrdeutigkeiten vorliegen, oder regionen einfach nicht passen wie sie sollten.
+		%\todo{obwohl das angepasste modell doch recht gut laeuft und der fehler recht klein wird, sind immernoch stellen dabei,
-		das liegt teils auch daran, dass die fingerprints drehend aufgenommen wurden und beim laufen nach hinten durch den
+		%wo es einfach nicht gut passt, unguenstige mehrdeutigkeiten vorliegen, oder regionen einfach nicht passen wie sie sollten.
-		menschen abgeschottet wird. auch zeitlicher verzug kann ein problem darstellen.}
+		%das liegt teils auch daran, dass die fingerprints drehend aufgenommen wurden und beim laufen nach hinten durch den
+		%menschen abgeschottet wird. auch zeitlicher verzug kann ein problem darstellen.}
 		
-		\todo{GPS ist leider kaum eine hilfe. entweder kein empfang wegen ueberdachung oder abschattung, oder
+		%\todo{
-		zu kurz draußen um einen guten gps-fix zu bekommen.}
+		%	wenn ich beim fingerprinten einen AP an einer stelle NICHT gesehen habe,
+		%	ist das auch eine aussage für die model optimierung.. da kann dann sicher keine signatlstaerke > -90 an der stelle raus kommen
+		%}
 		
-		\todo{
-			walk1 hat eine issue kurz bevor man zur tuer zum hoersaalgebaude reingeht
-			je nach resampling killt dieser wlan error evtl alle partikel!
-		}
 
-		\todo{
+		%ware das grid-model nicht da, wuerde der outdoor teil richtig schlecht laufen,
-			das bbox modell hat probleme an den uebergängen zwischen bboxes da dort teils starke spruenge sind
+		%weil das wlan hier absolut ungenau ist.. da die partikel aber aufgrund des vorherigen
-			die nicht immer in der realität so auch vorliegen. z.B. z-wechsel machen teils probleme.
+		%walks schon recht dicht beisamen sind, kittet das das ganze sehr gut.
-			hier wäre ein kontinuierliches modell hilfreich bzw interpolation in randbereichen
+		%kann man testen, indem man z.B. weniger resampling macht und mehr alte partikel aufhebt.
-		}
+		%geht sofort kaputt sobald man aus dem gebäude raus kommt
-
-		\todo{
-			wenn ich beim fingerprinten einen AP an einer stelle NICHT gesehen habe,
-			ist das auch eine aussage für die model optimierung.. da kann dann sicher keine signatlstaerke > -90 an der stelle raus kommen
-		}
-		
-		\todo{gps wird so schnell nicht warm, versagt denn auf dem hof als hilfestellung}
 
 
 
+		% was ist das??
+		%\input{gfx/wifi-opt-error-hist-methods.tex}
+		%\input{gfx/wifi-opt-error-hist-stair-outdoor.tex}
+		%outdoor hat insgesamt nicht all zu viel einfluss, da die meisten APs
+		%an den outdoor punkten kaum gesehen werden. auf einzelne APs kann
+		%der einfluss jedoch recht groß sein, siehe den fingerprint plot von
+		%dem einen ausgewählten AP
 
 
-ware das grid-model nicht da, wuerde der outdoor teil richtig schlecht laufen,
+	%\todo{anfaenglich falsches heading ist gift, wegen rel. heading, weil sich dann alles verlaeuft. fix: anfaenglich große heading variation erlauben}
-weil das wlan hier absolut ungenau ist.. da die partikel aber aufgrund des vorherigen
-walks schon recht dicht beisamen sind, kittet das das ganze sehr gut.
-kann man testen, indem man z.B. weniger resampling macht und mehr alte partikel aufhebt.
-geht sofort kaputt sobald man aus dem gebäude raus kommt
 
-signalstaerke limitieren, wie : alles was im model oder scan < -90 ist, wird auf -90 abgeschnitten hilft
+	%\todo{NICHT MEHR AKTUELL: abs-head ist in der observation besser, weil es beim resampling mehr bringt und dafuer srogt, dass die richtigen geloescht werden!}
-zwar an manchen stellen, im groben und ganzen führt es aber eher zu fehlern als zu verbesserungen.
-zudem ist zu erwarten, dass diese zahl stark vom geraet/hardware abhaengt
 
-jeweils beim weighting die niedrigste wifi probability weglassen [je nach particle also ein anderer AP]
-bringt auch nicht immer was.. killt gelegentlich floor-changes. zudem stehen am ende nur sehr wenige
-APs zur verfügung. da einen zu ignorieren, macht noch mehr kaputt
-
-auch ein versuch wie werfe alle APs aus dem handy-scan weg, die kleiner -90 sind, birgt die selben risiken
-es scheint wirklich am sinnvollsten, die scan-daten einfach 1:1 zu nehmen wie sie sind
-
-
-kurz vor ende von path 2 will die estimation nicht in die cafeteria, weil ein paar particle
-die treppe richtung h.1.5 hochgehen und durch das wlan sehr sehr hoch gewichtet werden.
-die mittelwert-estimation versagt hier
-
-
-% was ist das??
-%\input{gfx/wifi-opt-error-hist-methods.tex}
-%\input{gfx/wifi-opt-error-hist-stair-outdoor.tex}
-%outdoor hat insgesamt nicht all zu viel einfluss, da die meisten APs
-%an den outdoor punkten kaum gesehen werden. auf einzelne APs kann
-%der einfluss jedoch recht groß sein, siehe den fingerprint plot von
-%dem einen ausgewählten AP
-
-
-\todo{anfaenglich falsches heading ist gift, wegen rel. heading, weil sich dann alles verlaeuft. fix: anfaenglich große heading variation erlauben}
-
-\todo{NICHT MEHR AKTUELL: abs-head ist in der observation besser, weil es beim resampling mehr bringt und dafuer srogt, dass die richtigen geloescht werden!}
-
-\todo{ deutlich machen
-	wenn man nur die fingerprints des floors nimmt in dem gelaufen wird, ist alles gut
-sobald man andere floors drueber/drunter dazu nimmt, ist es nicht mehr gnaz so gut, oder wird schlechter
-das spricht dafuer dass das modell nicht gut passt
-koennte man zeigen indem man den durchschnittlichen fehler je fingerprint plottet???
-}

tex/chapters/system.tex

@@ -74,7 +74,7 @@
 	to further enhance the localization. Their values are incorporated by simply
 	comparing the sensor readings against a distribution that models the sensor's uncertainty.
 	
-	\todo{neues resampling? je nach dem was sich noch in der eval zeigt}
+	%\todo{neues resampling? je nach dem was sich noch in der eval zeigt}
 	
 	As GPS will only work outdoors, e.g. when moving from one building into another,
 	the system's absolute position indoors is solely provided by the \docWIFI{} component.

tex/chapters/work.tex

@@ -255,7 +255,7 @@
 
 
 
-\todo{???
+%\todo{???
-aps sind (statistisch) unaebhaengig. d.h., jeder AP kann fuer sich optimiert werden.
+%aps sind (statistisch) unaebhaengig. d.h., jeder AP kann fuer sich optimiert werden.
-optimierung des gesamtsystems ist nicht notwendig.
+%optimierung des gesamtsystems ist nicht notwendig.
-}
+%}