evaluation stand

tex/chapters/experiments.tex

@@ -18,8 +18,6 @@ All walks start with a uniform distribution (random position and heading) as pri
 To allow the system to stabilize its initial state, the first few estimations are omitted from error calculations.
 Even though, the error during the following few seconds is expected to be much higher than the error when starting with a well known initial position and heading.
 
-\commentByToni{Absatz drunter muss ich noch rumschreiben.}
-
 The measurements were recorded using a Motorola Nexus 6 and a Samsung Galaxy S5.
 As the Galaxy's \docWIFI{} can not be limited to the \SI{2.4}{\giga\hertz} band only, its scans take much longer than those of the Nexus: \SI{3500}{\milli\second} vs. \SI{600}{\milli\second}.
 Additionally, the Galaxy's barometer sensor provides fare more inaccurate and less frequent readings than the Nexus does. 
@@ -33,20 +31,16 @@ As mentioned earlier, the position of all \docAP{}s (about 5 per floor) is known
 Due to legal terms, we are not allowed to depict their positions and therefore omit this information within the figures.
 Additionally, we used five \docIBeacon{}s for slight enhancements in some areas.
 The empirically chosen values for \docWIFI{} were $P_{0_{\text{wifi}}} = \SI{-46}{\dBm}, \mPLE_{\text{wifi}} = \SI{2.7}{}, \mWAF_{\text{wifi}} = \SI{8}{\dB}$, and $\mPLE_{\text{ib}} = \SI{1.5}{}$ for the \docIBeacon{}s, respectively.
-Due to omitting a time-consuming calibration process for those values we expect the localisation process to perform generally worse compared to standard fingerprinting methods \cite{Ville09}. 
-%However, incorporating prior knowledge will often compensate for those poorly chosen system parameters.
+The system was tested by omitting any time-consuming calibration processes for those values.
+We therefore expect the localisation process to perform generally worse compared to standard fingerprinting methods \cite{Ville09}. 
+However, incorporating prior knowledge and smoothing will often compensate for those poorly chosen system parameters.
 
-\commentByToni{Hier eure noetigen Werte eintragen.}
-As uncertainties we used $\sigma_\text{wifi} = \sigma_\text{ib} = 8.0$, both growing with each measurement's age.
+For the  filtering we used $\sigma_\text{wifi} = \sigma_\text{ib} = 8.0$ as uncertainties, both growing with each measurement's age.
 While the pressure change was assumed to be \SI{0.105}{$\frac{\text{\hpa}}{\text{\meter}}$}, all other barometer-parameters are determined automatically (see \ref{sec:sensBaro}). 
 The step size $\mStepSize$ for the transition was configured to be \SI{70}{\centimeter} with an allowed derivation of \SI{10}{\percent}. The heading deviation in \refeq{eq:transSimple}, \refeq{eq:transShortestPath} and \refeq{eq:transMultiPath} was \SI{25}{\degree}.
 Edges departing from the pedestrian's destination are downvoted using $\mUsePath = 0.9$.
 
 
-	%
-
-
-
 % all paths we evaluated
 \begin{figure}
 	\input{gfx/eval/paths}
@@ -58,6 +52,8 @@ Edges departing from the pedestrian's destination are downvoted using $\mUsePath
 \end{figure}
 
 
+%kurz zeigen das activity recognition was bringt
+
 %Smoothing mit großen lag kann die zeitliche information schwer halten. das liegt hauptsächlich daran, das im smoothing nur die relativen positionsinfos genutzt werden. das wi-fi wird nicht beachtet und deswegen können absolute justierungen der position (sprünge) nur sehr schlecht abgefedert werden. 
 
 

tex/chapters/smoothing.tex

@@ -33,11 +33,11 @@ Then the backward step for determining the smoothing distribution is carried out
 The weights are obtained through the backward recursion in line 9.
 \commentByFrank{mir (als laie) wird nicht klar: mache ich erst alle forwaertsschritte (also alles bis zum pfadende durchlaufen) und gehe dann von da rueckwaerts (so klingts etwas im text), oder gehe ich nach jedem forwartsschritt rueckwarts (so klingts im pseudocode)}
 
-\begin{algorithm}[t]
-    \caption{Forward-Backward Smoother
-		\commentByFrank{reihenfolge von $ \{ W^i_t, \vec{X}^i_t\}^N_{i=1}$ war oben andersrum. ned schlimm. nur wegen konsistenz :P}
-    }
+\commentByFrank{reihenfolge von $\{ W^i_t, \vec{X}^i_t\}^N_{i=1}$ war oben andersrum. ned schlimm. nur wegen konsistenz :P}
     \label{alg:forward-backwardSmoother}
+
+\begin{algorithm}[t]
+    \caption{Forward-Backward Smoother}
     \begin{algorithmic}[1] % The number tells where the line numbering should start
 			\For{$t = 1$ \textbf{to} $T$} \Comment{Filtering}
 				\State{Obtain the weighted trajectories $ \{ W^i_t, \vec{X}^i_t\}^N_{i=1}$}