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fixed some missing macros

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removed path from intro/related/filtering
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kazu
2016-05-09 10:22:48 +02:00
parent 769d78d7f6
commit 2655ae73ac
3 changed files with 43 additions and 34 deletions
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14
tex/chapters/introduction.tex
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@@ -6,10 +6,10 @@
Determining a position indoors is a challenging task.
Besides the complex architecture of many buildings, high accuracy needs to be achieved, especially for buildings with many small separated areas like shopping malls or office blocks.
In recent years, many different systems were presented to meet those requirements.
Especially Wi-Fi positioning and pedestrian dead reckoning (PDR) are very popular solutions.
Especially \docWIFI{} positioning and pedestrian dead reckoning (PDR) are very popular solutions.
Approaches based on PDR try to estimate the current position given the previous position and thus require an initial state.
However, this allows for cumulative errors and leads to an erroneous position estimation within a very short period.
By incorporating the absolute position information of Wi-Fi this drift can be corrected.
By incorporating the absolute position information of \docWIFI{} this drift can be corrected.
Additional improvements can be achieved by using environmental information about walls and obstacles provided by a floor map.
In most cases, probabilistic methods are used to incorporate those highly different sensor types.
@@ -37,7 +37,7 @@ For example, a barometer can be used to determine the probability of being on a
Despite the many advances made in the last years, nearly all systems suffer from more or less the same problems.
Like mentioned before, PDR suffers from an accumulating bias,
the signal of Wi-Fi gets attenuated by walls
the signal of \docWIFI{} gets attenuated by walls
%\commentByFrank{falls noch platz ist: noch mehr nachteile :P \docWIFI{} location estimation strongly depends on the quality of the signal-strength estimation model (oder fingerprinting) and the way the smartphone is held}
and the barometric pressure is highly affected by weather patterns and humidity
%\commentByFrank{spontane fenster/tuer oeffnung}
@@ -52,7 +52,7 @@ This temporal delay worsens the estimate immensely.
A solution to recover from such filter divergences faster, involves methods to re-initialize the filtering procedure \cite{Nurminen2014}.
However, even this can not completely prevent delays.
Another reason for possible time delays are slow sensor updates.
For example, most mobile devices restrict the Wi-Fi module to update only every few seconds, to save on battery.
For example, most mobile devices restrict the \docWIFI{} module to update only every few seconds, to save on battery.
%
\begin{figure}[t]
\centering
@@ -72,12 +72,14 @@ Due to inaccurate measurements and a PDR approach for evaluating the movement, t
Therefore, the weighted average position is somewhere in-between.
Only after the pedestrian turns right, the distribution is again unimodal, since moving through walls is impossible.
As one can imagine, this can lead to serious problems in big indoor environments.
Such a situation can be improved by incorporating future measurements (e.g. the right turn) or predictive information (e.g. the most likely path) to the filtering procedure \cite{Ebner-16}.
Such a situation can be improved by incorporating future measurements (e.g. the right turn)
%or predictive information (e.g. the most likely path)
to the filtering procedure \cite{Ebner-16}.
However, standard filtering methods are not able to use any future information and the possibilities to make a distant forecast are also limited \cite{robotics, Doucet11:ATO, chen2003bayesian}.
One very promising way to deal with these problems is smoothing.
Smoothing methods are able to make use of future measurements for computing their estimation.
By running backwards in time, they are also able to remove multimodalities and improve the overall localization result.
By running backwards in time, they are also able to remove multimodalities and improve the overall localisation result.
Since the problem of navigation, especially the representation of complex movement patterns, results in a non-linear and non-Gaussian state space, this work focuses mainly on smoothing techniques based on the broad class of MC methods.
%Of course, this excludes linear procedures like Kalman filtering.
Namely, forward-backward smoothing \cite{doucet2000} and backward simulation \cite{Godsill04:MCS}.