less activity rec

tex/chapters/experiments.tex

@@ -20,7 +20,7 @@ When passing a marker, the pedestrian clicked a button on the smartphone applica
 Between two consecutive points, a constant movement speed is assumed.
 Thus, the ground truth might not be \SI{100}{\percent} accurate, but fair enough for error measurements.
 The approximation error is then calculated by comparing the interpolated ground truth position with the current estimation.
-Especially in the context of smoothing, it is also very interesting to exclude the temporal delay from the error calculations and measure only the positional difference between estimated and ground truth path. 
+Especially in the context of smoothing, it is also very interesting to exclude the temporal delay and measure only the positional difference between estimated and ground truth path. 
 This provides a statement about the extent to which the smoothed path superficially improves compared to the filtered one. 
 
 All walks start with a uniform distribution (random position and heading) as prior for $\mStateVec_0$. 
@@ -55,26 +55,27 @@ Walking upstairs sets $ \mu_{\text{step}} = \SI{0.4}{\meter}$, $ \sigma_{\text{s
 
 
 %kurz zeigen das activity recognition was bringt
-\begin{figure}
-	\input{gfx/activity/activity_over_time}
-	\caption{The activities recognized for path 4. The misdetection in seg. 2 is cause by faulty pressure readings.}
-	\label{fig:activityRecognition}
-\end{figure}
-By adding the activity recognition the approximation error of the filter decreases by an average of \SI{XX}{\centimeter} for all 4 paths.
-Due to this additional knowledge, the state transition samples mostly depending upon the current activity and therefore limits the possibility of false floor changes.
-Fig. \ref{fig:activityRecognition} shows the recognized activities for path 4 using the Nexus 6. 
-Despite a short misdetection in seg. 2, caused by faulty pressure readings, the recognition can be considered to be very robust and accurate.
+%\begin{figure}
+%	\input{gfx/activity/activity_over_time}
+%	\caption{The activities recognized for path 4. The misdetection in seg. 2 is cause by faulty pressure readings.}
+%	\label{fig:activityRecognition}
+%\end{figure}
+%By adding the activity recognition the approximation error of the filter decreases by an average of \SI{XX}{\centimeter} for all 4 paths.
+%Due to this additional knowledge, the state transition samples mostly %depending upon the current activity and therefore limits the possibility of false floor changes.
+%Fig. \ref{fig:activityRecognition} shows the recognized activities for path 4 using the Nexus 6. 
+%Despite a short misdetection in seg. 2, caused by faulty pressure readings, the recognition can be considered to be very robust and accurate.
 
 %Fixed Interval Smoothing
-At first, both FBS and BS are compared in context of fixed-interval smoothing.
-As a reminder, fixed-interval smoother are using all observations until time $T$ therefore run offline, after the filtering procedure is finished.
-Thus, we calculate only the positional error between estimation and ground truth, since timely information are negligible. 
 %
 \begin{figure}
 	\input{gfx/particles/particles}
 	\caption{Comparison between the filtered and FBS particle set. Both have identical positions and are recorded at the same time step on path 4. The black dot indicates the estimation using the weighted arithmetic mean of all particles. The different colours represent the current weight of a certain particle.}
 	\label{fig:particles}
 \end{figure}
+At first, both FBS and BS are compared in context of fixed-interval smoothing.
+As a reminder, fixed-interval smoother are using all observations until time $T$ therefore run offline, after the filtering procedure is finished.
+Thus, we calculate only the positional error between estimation and ground truth, since timely information are negligible. 
+%
 In contrast to BS, the FBS is not able to improve the results using the weighted arithmetic mean for estimating the current position.
 Fig. \ref{fig:particles} illustrates the filtered and smoothed particle set at a certain time step on path 4. 
 It can be seen that the estimated position (black dot) for filtering and FBS is identical although the particle weights are highly different. 
@@ -137,7 +138,7 @@ Here, the BS is able to slightly improve the path, whereas the FBS follows the f
 By looking at fig. \ref{fig:lag_comp_path4} seg. 9 it seems that both smoothing methods are highly improving the error. 
 However, the approximation error in this area is similar to the filter and only the positional error decreases.
 This timely error is caused by a phenomenon we call Wi-Fi jump. 
-Especially in seg. 8 and 9 a big crowd was gathered and highly attenuated the Wi-Fi signal.
+Especially in seg. 8 and 9 a big crowd was gathered and highly attenuated the Wi-Fi signal. 
 For an excessive amount of time, the absolute location estimated by the Wi-Fi component got stuck in the middle of seg. 8 and therefore delayed the estimation.
 The next viable measurements were then provided at the end of seg. 9.
 This suggests that the here presented smoothing transition is able to improve the estimated path visibly, but does not compensate for those jumps in a timely manner.