Recovering from sample impoverishment in context of indoor localisation

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.