\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.

The proposed method 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 the easy adaptation to other localization 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}
