Iterated multiple particle filtering

In the literature, there are claims stating that particle filters cannot be used for complex systems because their random measures degenerate to single particles. While this is true for standard implementation of these filters, it does not hold true for alternative approaches. A new methodology based on the principle of divide and conquer has already been proposed, where the collapse of traditional particle filtering is avoided by setting an interconnected network of filters, each of them working on lower dimensional spaces. In this paper we propose an enhanced version of multiple particle filtering, which uses tools of game theory for improved performance of the overall system. Computer simulations show that the new approach outperforms both standard and multiple particle filters.