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On multitarget jump-Markov filters

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1 Author(s)
Ronald Mahler ; Unified Data Fusion Sciences, Inc., Eagan, Minnesota, U.S.A.

Multiple motion model (MMM) filters are a well-known approach for addressing rapidly maneuvering, noncooperative targets. Jump-Markov models provide the most well-known theoretical foundation for MMM filters. This paper addresses the problem of how to correctly generalize jump-Markov models to multitarget systems. Given this generalization, the jump-Markov version of the multisensor-multitarget Bayes filter is introduced. Then CPHD filter and PHD filter approximations of the jump-Markov multitarget Bayes filter are derived and compared with previous approaches.

Published in:

Information Fusion (FUSION), 2012 15th International Conference on

Date of Conference:

9-12 July 2012