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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
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tex/chapters/relatedwork.tex
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@@ -39,13 +39,13 @@ For example, in \cite{Platzer:2008} a particle smoother is used to reduce multim
%smoothing im bezug auf indoor
Nevertheless, there are some promising approaches for indoor localisation systems as well.
For example \cite{Nurminen2014} deployed a fixed-interval forward-backward smoother to improve the position estimation for non-real-time applications.
They combined Wi-Fi, step and turn detection, a simple line-of-sight model for floor plan restrictions and the barometric change within a particle filter.
They combined \docWIFI{}, step and turn detection, a simple line-of-sight model for floor plan restrictions and the barometric change within a particle filter.
The state transition samples a new state based on the heading change, altitude change and a fixed step length.
The experiments of \cite{Nurminen2014} clearly emphasize the benefits of smoothing techniques. The estimation error could be decreased significantly.
However, a fixed-lag smoother was discussed only in theory.
In the work of \cite{Paul2009} both fixed-interval and fixed-lag smoothing were presented.
They implemented Wi-Fi, binary infrared motion sensors, binary foot-switches and a potential field for floor plan restrictions.
They implemented \docWIFI{}, binary infrared motion sensors, binary foot-switches and a potential field for floor plan restrictions.
Those sensors were incorporated using a sigma-point Kalman filter in combination with a forward-backward smoother.
It was also proven by \cite{Paul2009}, that the fixed-lag smoother is slightly less accurate than the fixed-interval smoother, as one would expect from the theoretical foundation.
Unfortunately, even a sigma-point Kalman filters is after all just a linearisation and therefore not as flexible and suited for the complex problem of indoor localisation as a non-linear estimator like a particle filter.
@@ -54,7 +54,7 @@ Unfortunately, even a sigma-point Kalman filters is after all just a linearisati
%\commentByToni{Ich bin mir nicht sicher ob das eine Behauptung ohne jeglichen Hintergrund ist. Meiner Meinung nach ist das ziemlich weitreichend bekannt. Finde den Satz persoenlich ganz gut, weil er uns deutlich von dieser Arbeit abgrenzt und das ist wichtig.}
%\commentByFrank{hab zwar ka was das ist, aber vermutlich ist es auch normal-dist also unimodal? dann verweisen wir doch einfach auf fig1 mit dem zusatz: 'sowas geht garned erst'}.
%\commentByToni{Sigma Point Kalman Filter können mit Multimodalitäten umgehen... auch wenn sie linearisieren. frag nicht wie das gehen soll.}
Additionally, the Wi-Fi RSSI model requires known calibration points and is deployed using a remarkable number of access points for very small spaces.
Additionally, the \docWIFI{} RSSI model requires known calibration points and is deployed using a remarkable number of access points for very small spaces.
In our opinion this is not practical and does not suite real-world conditions.
Since humans with a specific destination in mind do not tend to change their directions randomly, we would further recommend adding a PDR-based transition to draw samples in a more directed manner instead of scattering them randomly in every direction.
@@ -63,10 +63,10 @@ Since humans with a specific destination in mind do not tend to change their dir
%\commentByFrank{man koennte die reihenfolge vlt umstellen, erst die ganzen filtering sachen beschreiben, map, activity, ... und on top of that two smoothing algorithms both implemented as fixed-interval and fixed-lag?}
The here presented approach will use two different smoothing algorithm, both implemented as fixed-interval and fixed-lag versions.
Further, our localisation system presented in \cite{Ebner-16} enables us to walk stairs and thus going into the third dimension.
Therefore, a regularly tessellated graph is utilized to avoid walls, detecting doors and recognizing stairs.
Within this work, this is additionally supported by a simple classification that detects the activities unknown, standing, walking and walking stairs.
Finally, we incorporate prior navigation knowledge by using syntactically calculated realistic human walking paths \cite{Ebner-16}.
This method makes use of the given destination and thereby provides a more targeted movement.
Therefore, a regularly tessellated graph is utilised to avoid walls, detecting doors and recognizing stairs.
Within this work, this is additionally supported by a simple classification that detects the activities unknown, standing, walking and walking stairs.
%Finally, we incorporate prior navigation knowledge by using syntactically calculated realistic human walking paths \cite{Ebner-16}.
%This method makes use of the given destination and thereby provides a more targeted movement.