Abstract
In this paper we show how to correct and improve the Belief Interacting Multiple Model filter (BIMM) proposed in 2009 by Nassreddine et al. for tracking maneuvering targets. Our improved algorithm, called PCR-BIMM is based on results developed in DSmT (Dezert-Smarandache Theory) framework and concerns two main steps of BIMM: 1) the update of the basic belief assignment of modes which is done by the Proportional Conflict Redistribution Rule no. 5 rather than Smets' rule (conjunctive rule); 2) the global target state estimation which is obtained from the DSmP probabilistic transformation rather than the commonly used Pignistic transformation. Monte-Carlo simulation results are presented to show the performances of this PCR-BIMM filter with respect to classical IMM and BIMM filters obtained on a very simple maneuvering target tracking scenario.
Highlights
In Fusion 2009 international conference, Nassreddine, Abdallah, and Denœux [13] have proposed an interesting idea to extend the classical Interacting Multiple Models (IMM) filter with belief function theory in order to deal with an unknown and variant motion models
To preserve the potential advantages of Belief Interacting Multiple Model algorithm (BIMM) and to overcome its aforementionned problems, we propose to keep its general structure as a belief-based extension of classical IMM but we replace Smets’ rule by the more effective Proportional Conflict Redistribution rule no. 5 (PCR5), or eventually the more simple PCR rule no. 6 (PCR6), and to replace the pignistic transformation by the more effective DSmP transformation to estimate modes probabilities required in the IMM filter
We have examined in details the recent BIMM algorithm and have corrected a mistake in it, and identified some of its limitations
Summary
Abstract – In this paper we show how to correct and improve the Belief Interacting Multiple Model filter (BIMM) proposed in 2009 by Nassreddine et al for tracking maneuvering targets. Our improved algorithm, called PCR-BIMM is based on results developed in DSmT (Dezert-Smarandache Theory) framework and concerns two main steps of BIMM: 1) the update of the basic belief assignment of modes which is done by the Proportional Conflict Redistribution Rule no. 5 rather than Smets’ rule (conjunctive rule); 2) the global target state estimation which is obtained from the DSmP probabilistic transformation rather than the commonly used Pignistic transformation. Monte-Carlo simulation results are presented to show the performances of this PCR-BIMM filter with respect to classical IMM and BIMM filters obtained on a very simple maneuvering target tracking scenario
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