Tracking targets with pairwise-Markov dynamics
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© 2015 IEEE. Single- and multi-target tracking are both typically based on the hidden Markov chain (HMC) model. That is, the target process is a Markov chain, observed by an independent observation process. Since HMC independence assumptions are invalid in many practical applications, the pairwise Markov chain (PMC) model has been proposed as an approach for weakening them. Petetin and Desbouvries subsequently proposed a PMC generalization of the probability hypothesis density (PHD) filter, but their derivation was somewhat heuristic. The first major purpose of this paper is to construct a solid theoretical foundation for the Petetin-Desbouvries filter - which turns out to be a multitarget HMC model rather than a true multitarget PMC model The second major purpose is to use this foundation to devise PMC versions of any random finite set (RFS) filter, thus allowing tracking of targets with non-HMC dynamics.
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Pasha, S.; Vo, Ba-Ngu; Tuan, H.; Ma, W. (2009)The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. ...
Mahler, Ronald (2015)© 2015 SPIE. Single-and multi-target tracking are both typically based on strong independence assumptions regarding both the target states and sensor measurements. In particular, both are theoretically based on the hidden ...
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