further small changes

tex_review/chapters/experiments.tex

@@ -69,7 +69,7 @@ It implements the standard Android sensor functionalities and provides a very si
 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 \add{\SI{8}{threads} on \SI{4}{cores}} and \SI{16}{GB} main memory. 
 \add{An offline computation has practical advantages, such as easier evaluation of the results or shorter waiting times due to higher computing power. 
-Nevertheless, Android App and offline application are both based on the same C++ backend for localization.}
+Nevertheless, Android app and offline application are both based on the same C++ backend for localization.}
 
 
 %However, similar to our \add{previously presented system}, the setup is able to run completely on commercial smartphones as it \add{is} written in high performant C++ code \cite{torres2017smartphone}. 
@@ -118,7 +118,9 @@ Finally, the respective estimation methods are discussed in section \ref{sec:eva
 \end{figure}
 
 To compare our old graph-based model with our novel transition model presented within section \ref {sec:transition}, we chose a simple scenario, in which a tester walks up and down a staircase several times. 
-We used 1000 particles and did not perform an evaluation and resampling step to maintain the pure performance of the transition (step and heading).
+We used \SI{5000}{} particles and did not perform an evaluation and resampling step to maintain the pure performance of the transition (step and heading).
+\add{The number of particles was heuristically chosen and is based on our previous experience from other scenarios and competitions.
+In addition, it sill allows a stable performance of our Android app for localization.}
 The filter starts at a fixed position and is updated after every newly recognized step. 
 We set $\sigma_\text{step} = 0.1$ and $\sigma_\text{turn} = 0.1$ likewise. 
 The cells of the gridded graph were \SI{20}{} x \SI{20}{\centi\meter} in size and the transition implemented as described in \cite{Ebner-16}. 
@@ -268,7 +270,7 @@ Here, we differ between the respective anti-impoverishment techniques presented
 The simple anti-impoverishment method is added to the resampling step and thus uses the transition method presented in chapter \ref{sec:transition}.
 In contrast, the $D_\text{KL}$-based method extends the transition and thus uses a standard cumulative resampling step. 
 We set $l_\text{max} =$ \SI{-75}{dBm} and $l_\text{min} =$ \SI{-90}{dBm}.
-For a better overview, we only used the KDE-based estimation, as the errors compared to the weighted-average estimation differ by only a few centimeter.
+For a better overview, we only used the KDE-based estimation, as the errors compared to the weighted-average estimation differ by only a few centimeter. 
 
 \begin{table}[t]
 	\centering
@@ -310,7 +312,7 @@ Walking down the stairs at \SI{80}{\second} does also recover the localization s
 \begin{figure}
 	\centering
 	\input{gfx/errorOverTimeWalk0/errorOverTime.tex}
-	\caption{Error development over time of a single Monte Carlo run of walk 0. Between \SI{10}{\second} and \SI{24}{\second} the Wi-Fi signal was highly attenuated, causing the system to get stuck and producing high errors. Both, the simple and the $D_\text{KL}$ anti-impoverishment method are able to recover early. However, between \SI{65}{\second} and \SI{74}{\second} the simple method produces high errors due to the high random factor involved.} 
+	\caption{Error development over time of a single particle filter run of walk 0. Between \SI{10}{\second} and \SI{24}{\second} the Wi-Fi signal was highly attenuated, causing the system to get stuck and producing high errors. Both, the simple and the $D_\text{KL}$ anti-impoverishment method are able to recover early. However, between \SI{65}{\second} and \SI{74}{\second} the simple method produces high errors due to the high random factor involved.} 
 	\label{fig:errorOverTimeWalk0}
 \end{figure}
 
@@ -379,7 +381,8 @@ Ironically, this is again some type of sample impoverishment, caused by the afor
 \subsection{Activity Recognition}
 \label{sec:eval:act}
 
-Wie gut ist die Activity...
+\commentByToni{Wie gut ist die Activity...}
+
 
 
 %%estimation
@@ -421,7 +424,7 @@ Due to a right turn the lower red particles are walking against a wall and thus
 
 Although, situations as displayed in fig. \ref{fig:walk1:kde} frequently occur, the KDE-estimation is not able to improve the overall estimation results.
 This can be seen in the corresponding error development over time plot given by fig. \ref{fig:walk1:kdeovertime}. 
-Here, the KDE-estimation performs slightly better then the weighted-average, however after deploying \SI{100}{} Monte Carlo runs, the difference becomes insignificant.
+Here, the KDE-estimation performs slightly better then the weighted-average, however after deploying \SI{100}{} runs of the particle filter, the difference becomes insignificant.
 It is obvious, that the above mentioned "correct" mode, not always provides the lowest error. 
 In some situations the weighted-average estimation is often closer to the ground truth. 
 Within our experiments this happened especially when entering or leaving thick-walled rooms, causing slow and attenuated Wi-Fi signals. 
@@ -472,7 +475,8 @@ We hope to further improve such situations in future work by enabling the transi
 
 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 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.
+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. 
+\add{A detailed examination of the runtime performance of the used estimation methods in comparison to the state-of-the-art can be found in \cite{Bullmann-18}.}
 
 \commentByToni{Diskussion, wie die Contributions uns jetzt geholfen haben. Nochmal zusammengefasst.}
 

tex_review/chapters/relatedwork.tex

@@ -80,13 +80,14 @@ The estimated parameters can be refined using additional walks.
 Within this work we present a similar optimization approach for estimating the AP's location in 3D. 
 However, instead of taking multiple measuring walks, the locations are optimized based only on some reference measurements, further decreasing the setup-time.
 Additionally, we will show that such an optimization scheme can partly compensate for the above abolished intersection-tests.
+\commentByToni{Die Quelle aus den Reviews. Wir können auch Kontinuierlich. Der hat das Problem das er entweder überall gewesen sein muss, oder interpolieren.}
 
 %immpf
 Besides well chosen probabilistic models, the system's performance is also highly affected by handling problems which are based on the nature of \add{a} particle filter.
 They are often caused by restrictive assumptions about the dynamic system, like seen from the aforementioned problem of sample impoverishment. 
 The authors of \cite{Sun2013} handled the problem by using an adaptive number of particles instead of a fixed one. 
 The key idea is to choose a small number of samples if the distribution is focused on a small part of the state space and a large number of particles if the distribution is much more spread out and requires a higher diversity of samples.
-The problem of sample impoverishment is then addressed by adapting the number of particles dependent upon the system's current uncertainty \cite{Fetzer-17}.
+The problem of sample impoverishment is then addressed by adapting the number of particles dependent upon the system's current uncertainty.
 %\commentByFrank{ich glaube encountered ist das falsche wort. du willst doch auf 'es wird gefixed' raus, oder? addressed? mitigated?}
 
 In practice, sample impoverishment is often a problem of environmental restrictions and system dynamics.
@@ -98,8 +99,8 @@ Thus, a much simpler, but heuristic method is presented within this paper.
 
 %estimation
 Finally, as the name recursive state estimation says, it requires to find the most probable state within the state space, to provide the "best estimate" of the underlying problem.
-In the discrete manner of a particle representation this is often done by providing a single value, also known as sample statistic, to serve as a best guess \cite{Bullmann-18}. 
-Examples are the weighted-average over all particles or the particle with the highest weight.
+In the discrete manner of a particle representation this is often done by providing a single value, also known as sample statistic, to serve as a best guess \cite{bar2004estimation}. 
+Examples are the weighted-average over all particles or the particle with the highest weight \cite{blanco2009phd}.
 However, in complex scenarios like a multimodal representation of the posterior, such methods fail to provide an accurate statement about the most probable state.
 Thus, in \cite{Bullmann-18} we present a \del{rapid computation} \add{approximation} scheme of kernel density estimates (KDE).
 Recovering the probability density function using an efficient KDE algorithm yields a promising approach to solve the state estimation problem in a more profound way.

tex_review/egbib.bib

@@ -2177,10 +2177,10 @@ IGNOREmonth={Apr},
 @PHDTHESIS{blanco2009phd,
     author = {Blanco, Jos{\'{e}}-Luis},
      IGNOREmonth = {{nov}},
-     title = {Contributions to Localization, Mapping and Navigation in Mobile Robotics},
+     title = {{Contributions to Localization, Mapping and Navigation in Mobile Robotics}},
       year = {2009},
     school = {PhD. in Electrical Engineering, University of Malaga},
-       url = {http://www.mrpt.org/Paper:J.L._Blanco_Phd_Thesis}
+       IGNOREurl = {http://www.mrpt.org/Paper:J.L._Blanco_Phd_Thesis}
 }
 
 @article{kunsch2005recursive,
@@ -2990,3 +2990,10 @@ address = {{Rothenburg, Germany}},
 	REM_ISSN={2153-0866},
 	REM_month={Sept},
 }
+
+@book{bar2004estimation,
+  title={{Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software}},
+  author={Bar-Shalom, Yaakov and Li, X Rong and Kirubarajan, Thiagalingam},
+  year={2004},
+  publisher={John Wiley \& Sons}
+}