Small fixes

tex/chapters/abstract.tex

@@ -10,7 +10,7 @@ We further tackle advanced problems like multimodal densities and sample impover
 \newline
 The goal of this work is to propose a fast to deploy and low-cost localization solution, that
 provides reasonable results in a high variety of situations.
-Therefore, we have chosen a very challenging test scenario. 
+To stress our system, we have chosen a very challenging test scenario. 
 All experiments were conducted within a 13th century historic building, formerly a convent and today a museum. 
 The system is evaluated using 28 distinct measurement series on four different test walks, up to \SI{310}{\meter} length and \SI{10}{\minute} duration.
 It can be shown, that the here presented localization solution is able to provide a small positioning error, even under difficult conditions and faulty measurements. 

tex/chapters/conclusion.tex

@@ -3,7 +3,7 @@
 %what you have seen
 Within this work we provided an extensive overview of our smartphone-based indoor localization system. 
 The thorough evaluation demonstrated the good performance under multiple scenarios within a complex environment. 
-The system is able to handle problems like sample impoverishment and multimodal densities, occurring through the us of a particle filtering scheme. 
+The system is able to handle problems like sample impoverishment and multimodal densities, occurring through the use of a particle filtering scheme. 
 The main advantage of our approach is its suitability for practical use.
 Compared to other state-of-the-art solutions, the setup time is only a few hours and does not require any expert knowledge or hardware. 
 The localization runs solely an a commercial smartphone, thus no connection to a server or the Wi-Fi infrastructure is required.
@@ -17,7 +17,7 @@ Especially in buildings where elevators pass many floors, the transition fails t
 Here, we need to incorporate special environmental knowledge about elevators and escalators or again integrate a probabilistic sensor model for the barometer as already done in previous works \cite{Ebner-15}. 
 
 A crucial point to further increase the accuracy of the system is the choice of the signal strength prediction model. 
-At the moment we consider only the attenuation per floor, however by including information about walls and other obstacles, we should be able to decrease the error at the cost of additional computations. 
+Currently we consider only the attenuation per floor, however by including information about walls and other obstacles, we should be able to decrease the error at the cost of additional computations. 
 Instead of providing those additional environmental informations by manual measurements, the optimization scheme could be used to approximate the respective model and material parameters. 
 Special data-structures for pre-computation combined with online interpolation might then be a viable choice for utmost accuracy that is still able to run on a commercial smartphone in real-time.
 

tex/chapters/estimation.tex

@@ -29,10 +29,10 @@ In the case of particle filters the MMSE estimate equals to the weighted-average
 where $W_t=\sum_{i=1}^{N}w^i_t$ is the sum of all weights.
 While producing an overall good result in many situations, it fails when the posterior is multimodal.
 In these situations the weighted-average estimate will find the estimate somewhere between the modes.
-Clearly, such a position between modes is extremely unlikely the real position of the pedestrian.
+Clearly, such a position between modes is extremely unlikely the position of the pedestrian.
 The real position is more likely to be found at the position of one of the modes, but virtually never somewhere between.
 
-In the case of a multimodal posterior the system should estimate the position based on the most highest mode.
+In the case of a multimodal posterior the system should estimate the position based on the highest mode.
 Therefore, the maximum a posteriori (MAP) estimate is a suitable choice for such a situation.
 A straightforward approach is to select the particle with the highest weight.
 However, this is in fact not necessarily a valid MAP estimate, because only the weight of the particle is taken into account.

tex/chapters/eval.tex

@@ -99,12 +99,13 @@ For example, if a pedestrian walks on a staircase and thus is in-between multipl
 %man muss zwar messungen machen, dafür muss man aber die position der ap's nicht mehr kennen. daher kostet das jetzt nicht viel mehr zeit.
 
 Basically, any kind of \docAPshort{} providing RSSI measurements can be used for the above. 
+\commentByMarkus{Provieded der AP die RSSI? Misst nicht das Smartphone an seiner Antenne?}
 However, most buildings do not provide a satisfying and well covered \docWIFI{} infrastructure, e.g. staircases or hallways are often neglected for office spaces. 
 This applies in particular to historical buildings, as discussed in section \ref{sec:intro}.
 To improve $\docWIFI$ coverage we are able to distribute a small number of simple and cheap \docWIFI{} beacons.
 As beacons we use a WEMOS D1 mini, which is based on the ESP-8266EX \docWIFI{} chip \cite{Wemos}. 
 The building considered in this work has no \docWIFI{} infrastructure at all, not even a single \docAPshort{}.
-Nevertheless, our method also allows to distribute beacons in the whole building by just plugging them into available power outlets.
+Nevertheless, our method allows to distribute beacons in the whole building by simply plugging them into available power outlets.
 
 
 \subsection{Activity Recognition}

tex/chapters/experiments.tex

@@ -16,7 +16,7 @@ It was created using our 3D map editor software based on architectural drawings
 Sensor measurements are recorded using a simple mobile application that implements the standard Android sensor functionalities.
 As smartphones we used either a Samsung Note 2, Google Pixel One or Motorola Nexus 6.
 The computation of the state estimation as well as the \docWIFI{} optimization are done offline using an Intel Core i7-4702HQ CPU with a frequency of \SI{2.2}{GHz} running \SI{8}{cores} and \SI{16}{GB} main memory. 
-However, similar to our previous, award-winning system, the setup is able to run completely on commercial smartphones as well as it uses C++ code \cite{torres2017smartphone}. 
+However, similar to our previous, award-winning system, the setup is able to run completely on commercial smartphones as it written in high performant C++ code \cite{torres2017smartphone}. 
 %Sensor measurements are recorded using a simple mobile application that implements the standard Android SensorManager. 
 
 The experiments are separated into four sections:
@@ -96,7 +96,7 @@ Besides the advantages the mesh offers, it also has a few disadvantages compared
 The computation time has increased due to the calculation of reachable destinations. 
 With the graph, only the direct neighbours are of interest, which can be implemented very efficiently using a tree structure.
 Further, the graph allows the easily store meta information on its nodes, for example Wi-Fi fingerprints or precalculations for shortest-path methods.
-This is more difficult using the mash and requires the handling of baricentric coordinates.
+This is more difficult using the mesh and requires the handling of baricentric coordinates.
 
 
 \subsection{\docWIFI{} Optimization}
@@ -185,11 +185,11 @@ looking at the optimziation errors, this can be varified... etc pp
 The 4 chosen walking paths can be seen in fig. \ref{fig:floorplan}. 
 Walk 0 is \SI{152}{\meter} long and took about \SI{2.30}{\minute} to walk.
 Walk 2 has a length of \SI{223}{\meter} and Walk 3 a length of \SI{231}{\meter}, both required about \SI{6}{\minute} to walk. 
-Finally, walk 3 is \SI{310}{\meter} long and needs \SI{10}{\minute} to walk.
-All walks were carried out be 4 different male testers using either a Samsung Note 2, Google Pixel One or Motorola Nexus 6 for recording the measurements. 
+Finally, walk 3 is \SI{310}{\meter} long and takes \SI{10}{\minute} to walk.
+All walks were carried out by 4 different male testers using either a Samsung Note 2, Google Pixel One or Motorola Nexus 6 for recording the measurements. 
 All in all, we recorded \SI{28}{} distinct measurement series, \SI{7}{} for each walk.
 The picked walks intentionally contain erroneous situations, in which many of the above treated problems occur. 
-Thus we are able to discuss everything in detail.  
+This allows us to discuss everything in detail.  
 A walk is indicated by a set of numbered markers, fixed to the ground. 
 Small icons on those markers give the direction of the next marker and in some cases provide instructions to pause walking for a certain time.
 The intervals for pausing vary between \SI{10}{\second} to \SI{60}{\second}. 
@@ -236,7 +236,7 @@ We discuss the single results of table \ref{table:overall} starting with walk 0.
 Here, the pedestrians started at the top most level, walking down to the lowest point of the building. 
 The first critical situation occurs immediately after the start.
 While walking down the small staircase, many particles are getting dragged into the room to the right due to erroneous Wi-Fi readings. 
-At this point, the activity "walking down" is recognized, however only a for very short period. 
+At this point, the activity "walking down" is recognized, however only for a very short period. 
 This is caused by the short length of the stairs.
 After this period, only a small number of particles changed the floor correctly, while a majority is stuck within the right-hand room.
 The activity based evaluation $p(\vec{o}_t \mid \vec{q}_t)_\text{act}$ prevents particles from further walking down the stairs, while the resampling step mainly draws particles in already populated areas.
@@ -278,13 +278,14 @@ Only two \docAPshort{}'s provide a solid signal within this area, leading to a h
 
 \begin{figure}[t!]
 	\centering
-  	\begin{subfigure}[t]{0.48\textwidth}
+  	\begin{subfigure}[t]{0.45\textwidth}
 		\def\svgwidth{\columnwidth}
 	    {\input{gfx/wifiOptGlobalFloor/wifiOptGlobalFloor.eps_tex}}
         \caption{}
         \label{fig:walk3:wifiopt}
     \end{subfigure}
-	\begin{subfigure}[t]{0.50\textwidth}
+    \hfil
+	\begin{subfigure}[t]{0.45\textwidth}
 		\resizebox{1\textwidth}{!}{\input{gfx/errorOverTimeWalk3/errorOverTime.tex}}
         \caption{}
         \label{fig:walk3:time}
@@ -330,12 +331,13 @@ Regarding the underlying particle set, different shapes of probability distribut
 %
 \begin{figure}[t]
 	\centering
-	\begin{subfigure}{0.48\textwidth}
+	\begin{subfigure}{0.45\textwidth}
 		\resizebox{1\textwidth}{!}{\input{gfx/walk.tex}}
         \caption{}
         \label{fig:walk1:kde}
     \end{subfigure}
-	\begin{subfigure}{0.50\textwidth}
+    \hfil
+	\begin{subfigure}{0.45\textwidth}
 		\resizebox{1\textwidth}{!}{\input{gfx/errorOverTimeWalk1/errorOverTime.tex}}
         \caption{}
         \label{fig:walk1:kdeovertime}
@@ -370,7 +372,7 @@ We have highlighted some interesting areas with coloured squares.
 The greatest difference between the respective estimation methods can be seen inside the green square, the gallery wing of the museum.
 While the weighted-average (blue) produces a very straight estimated path, the KDE-based method (red) is much more volatile. 
 This can be explained by the many small rooms that pedestrians pass through. 
-The doors act like a bottleneck, which is why many particles run against walls and are thus either drawn on a new position within a reachable area (cf. section \ref{sec:estimation}) or walk along the wall towards the door.
+The doors act like bottlenecks, which is why many particles run against walls and thus are either drawn on a new position within a reachable area (cf. section \ref{sec:estimation}) or walk along the wall towards the door.
 This causes a higher uncertainty and diversity of the posterior, what is more likely to be reflected by the KDE method than by the weighted-average.
 Additionally, the pedestrian was forced seven times to look at paintings (stop walking) between \SI{10}{\second} and \SI{20}{\second}, just in this small area. 
 Nevertheless, even if both estimated paths look very different, they produce similar errors. 
@@ -380,13 +382,13 @@ Due to a poorly working \docAPshort{}, in the lower corner of the big room the p
 By allowing some particles to walk through the wall and thus down the stairs, the impoverishment could be dissolved. 
 The KDE-based estimation illustrates this behaviour very accurate.
 Another situation in which the estimated paths do not provide sufficient results can be seen inside the teal square. 
-The room is very isolated from the rest of the building, which is reflected in the fact that only 3 \docAPshort{}'s are detected.
+The room is very isolated from the rest of the building, which is reflected by the fact that only 3 \docAPshort{}'s are detected.
 The pedestrians have been asked to cross the room at a quick pace, leading to a higher step rate and therefore update rate of the filter.
 The results within this area lead to the assumption, that even if Wi-Fi has a bad coverage, it influences the estimation results the most. 
 The PDR based transition alone is able to walk alongside the ground truth in an accurate manner. 
 However, this is of course only true if we consider this area individually, without the rest of the walk due to the accumulating bias of the relative sensors involved.
 In the end, it is a question of optimal harmony between transition and evaluation. 
-We hope to further improve such situations in future work by enabling the transition step to provide a weight to particles that walk very likely, especially in situation were Wi-Fi provides bad readings.
+We hope to further improve such situations in future work by enabling the transition step to provide a weight to particles that walk very likely, especially in situation where Wi-Fi provides bad readings.
 
 \begin{figure}[t]
 	\centering
@@ -397,7 +399,7 @@ We hope to further improve such situations in future work by enabling the transi
 \end{figure}
 
 To summarize, the KDE-based approach for estimation is able to resolve multimodalities. 
-It does not provide a smooth estimated path, since it depends more on an accurate sensor model then a weighted-average approach, but is very suitable as a good indicator about the real performance of a sensor fusion system. 
+It does not provide a smooth estimated path, since it depends more on an accurate sensor model than a weighted-average approach, but is suitable as a good indicator about the real performance of a sensor fusion system. 
 At the end, in the here shown examples we only searched for a global maxima, even though the KDE approach opens a wide range of other possibilities for finding a best estimate.
 
 

tex/chapters/introduction.tex

@@ -1,7 +1,7 @@
 \section{Introduction}
 \label{sec:intro}
 
-Setting up a localization solution for a building is a challenging and time-consuming task, especially in environments that are not built with localization in mind or do not provide any wireless infrastructure or even both. 
+Setting up a reliable localization solution for a building is a challenging and time-consuming task, especially in environments that are not built with localization in mind or do not provide any wireless infrastructure or even both. 
 Such scenarios are of special interest when old or historical buildings serve a new purpose such as museums, shopping malls or retirement homes.
 In terms of European architecture, the problems emanating from these buildings worsen over time.
 
@@ -12,25 +12,25 @@ Since 1936, the \SI{2500}{\square\meter} building acts as a museum of the mediev
 
 Such buildings are often full of nooks and crannies, what makes it hard for dynamical models using any kind of pedestrian dead reckoning (PDR). Here, the error accumulates not only over time, but also with the number of turns and steps made \cite{Ebner-15}. 
 There is also a higher chance of detecting false or misplaced turns, what can cause the position estimation to lose track or getting stuck within a demarcated area. 
-Thus, this paper presents a very robust but realistic movement model using a three-dimensional navigation mesh based on triangles.
+Thus, this paper presents a robust but realistic movement model using a three-dimensional navigation mesh based on triangles.
 %In addition, this allows for very small map sizes, consuming little storage space.
 
 In localization systems using a sample based density representation, like particle filters, aforementioned problems can further lead to more advanced problems like sample impoverishment \cite{Fetzer-17} or multimodalities \cite{Fetzer-16}.
 Sample impoverishment refers to a situation, in which the filter is unable to sample enough particles into proper regions of the building, caused by a high concentration of misplaced particles. 
 Within this work we present a simple yet efficient method that enables a particle filter to fully recover from sample impoverishment. 
-We also use a novel approach for finding an exact estimation of the pedestrian's current position by using a rapid computation scheme of the kernel density estimation.
+We also use a novel approach for finding an exact estimation of the pedestrian's current position by using a rapid computation scheme of the kernel density estimation \cite{Bullmann-18}.
 
 Many historical buildings, especially bigger ones like castles, monasteries or churches, are built of massive stone walls and have annexes from different historical periods out of different construction materials.
 This leads to problems for methods using received signal strengths (RSS) from \docWIFI{} or Bluetooth, due to a high signal attenuation between different rooms. 
 Many unknown quantities like the walls definitive material or thickness make it expensive to determine important parameters, \eg{} the signal's depletion  over distance. 
-Additionally, most wireless approaches require a line-of-sight assumption. 
+Additionally, most wireless approaches are based on a line-of-sight assumption. 
 Thus, the performance will be even more limited due to the irregularly shaped spatial structure of such buildings. 
 Our approach tries to avoid those problems. 
 We distribute a small number of simple and cheap \docWIFI{} beacons over the whole building and instead of measuring their position, we use an optimization scheme based on some reference measurements.
 
 An optimization scheme also avoids inaccuracies like wrongly positioned access points or fingerprints caused by outdated or inaccurate building plans. 
 It is obvious, that this could be solved by re-measuring the building, however this is a very time-consuming process requiring specialized hardware and a surveying engineer. 
-However, this is contrary to most costumers expectations of a fast to deploy and low-cost solution.
+Clearly, this is contrary to most costumers expectations of a fast to deploy and low-cost solution.
 In addition, this is not only a question of costs incurred, but also for buildings under monumental protection, what does not allow for larger construction measures. 
 
 To sum up, this work presents a smartphone-based localization system using a particle filter to incorporate different probabilistic models.

tex/chapters/misc.tex

@@ -98,6 +98,7 @@ The quality factor is defined by
 \noindent where $\bar\mRssi_\text{wifi}$ is the average of all signal strength measurements received from the observation $\mObsVec_t^{\mRssiVec_\text{wifi}}$. An upper and lower bound is given by $l_\text{max}$ and $l_\text{min}$. 
 The quality factor is extensively discussed within \cite{Ebner-17} and \cite{Fetzer-17}.
 
+\commentByMarkus{Nochmal eine second method, meintest du third? wenn nicht versteh ich den Satz hier oder oben nicht}
 Finally, we have all necessary tools to introduce a second method to prevent impoverishment. 
 For this, the state transition model is extended. 
 Compared to the resampling step, as used by the first method, the transition $p(\mStateVec_{t} \mid \mStateVec_{t-1}, \mObsVec_{t-1})$ enables us to use prior measurements, which is obviously necessary for all \docWIFI{} related calculations.

tex/chapters/relatedwork.tex

@@ -36,7 +36,7 @@ Smooth 3D transitions, like walking stairs, would require much more complex inte
 To overcome both limitations, the building's floorplan can be used to derive a graph-based structure,
 like voronoi diagrams or fixed-distance grids, moving all costly intersection tests into a one-time offline phase \cite{Ebner-16, Hilsenbeck2014}.
 Hereafter, graph-based random walks along the created data-structure can be used as a fast transition approximation.
-When generating nodes and edges along stairs and elevators, this also allows for smooth transitions in 3D space.
+Smooth transitions in 3D space can be achieved by generating nodes and edges along stairs and elevators.
 Furthermore, the nodes can be used to store additional information, like their distance towards a pedestrian's desired destination.
 Such information can be included during the transitions step, \eg{} increasing the likelihood of all potential movements that approach
 this destination \cite{Ebner-16}.
@@ -73,10 +73,10 @@ While many of them are intended for outdoor and line-of-sight purposes \cite{Pre
 Besides their solid performance in many different localization solutions, a complex scenario requires a equally complex signal strength prediction model.
 As described in section 1, historical buildings represent such a scenario and thus the model has to take many different constraints into account.
 An example is the wall-attenuation-factor model \cite{PathLossPredictionModelsForIndoor}. 
-It introduces an additional parameter to the well-known log distance model \cite{IntroductionToRadio}, that considers obstacles between (line-of-sight) the AP and the location in question by attenuating the signal with a constant value. 
+It introduces an additional parameter to the well-known log distance model \cite{IntroductionToRadio}, which considers obstacles between (line-of-sight) the AP and the location in question by attenuating the signal with a constant value. 
 Depending on the use-case, this value describes the number and type of walls, ceilings, floors etc. between both positions. 
 For obstacles, this requires an intersection-test of each obstacle with the line-of-sight, which is costly for larger buildings. 
-Thus \cite{Ebner-17} suggests to only consider floors/ceilings, what can be calculated without intersection checks and allows for real-time use-cases running on smartphones.
+Thus \cite{Ebner-17} suggests to only consider floors/ceilings, which can be calculated without intersection checks and allows for real-time use-cases running on smartphones.
 
 %wifi optimization
 To further reduce the setup-time, \cite{WithoutThePain} introduces an approach that works without any prior knowledge.

tex/chapters/system.tex

@@ -2,7 +2,7 @@
 \label{sec:rse}
 
 We consider indoor localization to be a time-sequential, non-linear and non-Guassian state estimation problem. 
-The filtering equation to calculated the posterior is given by the recursion
+The filtering equation to calculate the posterior is given by the recursion
 %
 \begin{equation}
 	\arraycolsep=1.2pt