19 lines
1.6 KiB
TeX
19 lines
1.6 KiB
TeX
\begin{abstract}
|
|
|
|
In recent research, indoor localisation systems are often based upon a recursive state estimation using particle filtering.
|
|
Within this context, sample impoverishment is a crucial problem causing the position estimation to lose track or get stuck within a demarcated area.
|
|
The sample impoverishment problem can therefore be described as a too small particle diversity, unable to sample enough particles into proper regions of the dynamic system.
|
|
Restrictive transition models, as they are used in indoor localisation, also enhance this effect significantly.
|
|
However, an accurate position estimation requires a certain degree of focus and thus behaves contrary to the need of diversity.
|
|
|
|
Therefore we propose a new method that is able to deal with the trade-off between the need of diversity and focus by deploying an interacting multiple model particle filter (IMMPF) for jump Markov non-linear systems.
|
|
We combine two similar particle filters using a non-trivial Markov switching process, depending upon the Kullback-Leibler divergence and a Wi-Fi quality factor. The main benefit of this
|
|
approach is an easy adaptation to other localisation approaches based on particle filters.
|
|
|
|
|
|
%One with a very restrictive transition scheme, providing very accurate results. The other with more flexible and simple dynamics, resulting in a higher sample diversity.
|
|
|
|
%\commentByToni{TODO: Namen von Methoden gross oder klein? \\ Normalisierungsfaktoren dazu schreiben, oder langt Bemerkung im Text?}
|
|
\end{abstract}
|
|
%\begin{IEEEkeywords} indoor positioning, Monte Carlo smoothing, particle smoothing, sequential Monte Carlo\end{IEEEkeywords}
|