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Comments on RelatedWork, Smoothing, System

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Lukas Koeping
2016-05-02 10:39:51 +02:00
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tex/chapters/relatedwork.tex
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@@ -32,7 +32,7 @@ In recent years, smoothing gets attention mainly in other areas as indoor locali
The early work of \cite{isard1998smoothing} demonstrates the possibilities of smoothing for visual tracking.
They used a combination of the CONDENSATION particle filter with a forward-backward smoother.
Based on this pioneering approach, many different solutions for visual and multi-target tracking have been developed \cite{Perez2004}.
For example, in \cite{Platzer:2008} a particle smoother is used to reduce multimodalities in a blood flow simulation for human vessels. Or \cite{Hu2014} uses a smoother to overcoming the problem of particle impoverishment while predicting the Remaining Useful Life (RUL) of equipment (e.g. a Lithium-ion battery).
For example, in \cite{Platzer:2008} a particle smoother is used to reduce multimodalities in a blood flow simulation for human vessels. \commentByLukas{Or nicht schoen am Satzanfang. In [] a smoother is used to... oder the authors of [] use a ...}Or \cite{Hu2014} uses a smoother to overcoming the problem of particle impoverishment while predicting the Remaining Useful Life (RUL) of equipment (e.g. a Lithium-ion battery).
%smoothing im bezug auf indoor
Nevertheless, their are some promising approaches for indoor localisation systems as well.
@@ -45,14 +45,16 @@ However, a fixed-lag smoother was treated only in theory.
In the work of \cite{Paul2009} both fixed-interval and fixed-lag smoothing were presented.
They implemented Wi-Fi, binary infra-red 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 then the fixed-interval smoother.
It was also proven by \cite{Paul2009}, that the fixed-lag smoother is slightly less accurate then the fixed-interval smoother. \commentByLukas{, as one would expect ...}
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.
\commentByToni{Kann das jemand nochmal verifizieren? Das mit dem Kalman Filter. Danke.}
\commentByLukas{Ich wuerde den Satz ganz weglassen. Ansonsten musst du angeben, wo die eigentlichen Probleme liegen, also z.B. in welchen konkreten Situation das Kalman Filter nicht mehr funktioniert usw. So ist es jetzt erstmal nur eine Behauptung ohne jeglichen Hintergrund.}
Additionally, the Wi-Fi 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 we would further recommend adding a PDR-based transition instead of a random one.
\commentByLukas{Der erste Teil vom Satz bezieht sich auf die vielen Messungen? Das find ich ok. Der zweite Teil vom Satz hat dann damit aber gar nichts mehr zu tun? Auch hier muesste man begruenden warum eine zufaellige Transition schlecht ist.}
In contrast, the here presented approach is able to use two different smoothing algorithm, both implemented as fixed-interval and fixed-lag versions.
In contrast \commentByLukas{In contrast zu was? Wuerde ich weglassen. The here presented ...}, the here presented approach is able to 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 therefore 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.

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tex/chapters/smoothing.tex
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@@ -6,7 +6,7 @@ As mentioned before, those algorithm are able to compute probability distributio
%Especially fixed-lag smoothing is very promising in context of pedestrian localisation.
In the following we discuss the algorithmic details of the forward-backward smoother and the backward simulation.
Further, a novel approaches for incorporating them into the localisation system is shown.
Further, a novel approach for incorporating them into the localisation system is shown.
\subsection{Forward-backward Smoother}

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tex/chapters/system.tex
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@@ -34,7 +34,7 @@ covering all relevant sensor measurements.
Here, $\mRssiVec_\text{wifi}$ and $\mRssiVec_\text{ib}$ contain the measurements of all nearby \docAP{}s (\docAPshort{}) and \docIBeacon{}s, respectively.
$\mObsHeading$ and $\mObsSteps$ describe the relative angular change and the number of steps detected for the pedestrian.
$\mObsPressure$ is the relative barometric pressure with respect to a fixed reference.
Finally, $x$ contains the activity, currently estimated for the pedestrian, which is one of: unknown, standing, walking or walking stairs.
Finally, $x$ \commentByLukas{Vermutlich gerade nur Platzhalter. Aber x ueberschneidet sich mit dem x der Position. Wie waers mit $\Omega$} contains the activity, currently estimated for the pedestrian, which is one of: unknown, standing, walking or walking stairs.
The probability density of the state evaluation is given by
%