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final version paper

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toni
2017-05-12 16:37:19 +02:00
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@@ -20,7 +20,7 @@ However, in practice, sample impoverishment is also a problem of environmental r
Here, classical resampling schemes fail, since they are not able to propagate new particles into the state space.
More promising and intelligent solutions are given by techniques of Particle Distribution Optimization (PDO).
These variations of techniques are acting in different ways to optimize the spatial distribution of particles and are particularly effective in alleviating sample degeneracy and impoverishment \cite{Li2014}.
For example in \cite{Xiaoqin2008} a Particle Swarm Optimization is used as importance distribution for visual tracking.
For example, in \cite{Xiaoqin2008} a Particle Swarm Optimization is used as importance distribution for visual tracking.
Particles are iteratively updated according to their own experience and the experience of the swarm (or neighboring particles).
This allows for a multi-layer importance sampling and incorporation of the current measurements into the importance distribution, dealing with the sample impoverishment.
Other PDO methods are presented in \cite{Li2014}.
@@ -39,11 +39,11 @@ Thereby, they are able to provide a robust and stable position estimation with h
An extension to particle filters, and therefore to non-linear and non-Gaussian system, was presented by \cite{Boers2003}.
The so called Interacting Multiple Model Particle Filter (IMMPF) was then further developed by \cite{Driessen2005}, adding a direct sampling approach.
This allows a merging between different particle filters by providing a possibility for each filter to sample additional particles from all available particle sets and not just from its own.
This allows merging between different particle filters by providing a possibility for each filter to sample additional particles from all available particle sets and not just from its own.
It is obvious that the possibility to draw from other particle sets is based on the mode's probability and the transition matrix provided by the Markov Chain process and therefore does not violate the Markov property.
Now, the key idea of this work is to satisfy the trade-off between diversity and focus by using appropriate modes within the IMMPF.
Warum? Weil die meinsten loca systeme auf particle filtern basieren und deswegen bietet es sich an. es erlaubt bereits vorhandene methoden die auf die jeweils einzeln auf die probleme eingehen zu kombinieren und so ein hybrid zu schaffen.
%Warum? Weil die meinsten loca systeme auf particle filtern basieren und deswegen bietet es sich an. es erlaubt bereits vorhandene methoden die auf die jeweils einzeln auf die probleme eingehen zu kombinieren und so ein hybrid zu schaffen.
%Therefore, two different dynamical models are utilized and a novel approach for a non-trivial Markov switching Process based on Kullback-Leibler divergence and a Wi-Fi quality factor are presented.