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access icon free Multi-sensor Poisson multi-Bernoulli filter based on partitioned measurements

© The Institution of Engineering and Technology
Received 13/10/2019, Accepted 06/01/2020, Revised 03/12/2019, Published 08/01/2020

The single-sensor Poisson multi-Bernoulli (MB) mixture (PMBM) filter has been developed for multi-target tracking (MTT). However, there is a lack of research on the multi-sensor (MS) extensions of this filter. Because the conjugate density of PMBM filter is a hybrid form, which makes it difficult to extend directly using existing methods. In this study, a general MS Poisson MB filter based on an MS measurement likelihood is derived for MS-MTT. The MB mixture in the PMBM conjugate posterior is approximated as a single MB after each measurement update step. The likelihood function is designed for the partitioned measurements. Firstly, the authors employ the greedy measurement partition algorithm to derive an efficient implementation method; a Gibbs sampler is used to solve the data association problem subsequently. Secondly, they design a novel partition mechanism based on the Gibbs sampling algorithm dealing with those measurements generated by close targets. Various performance simulation and analysis are given in Sections 5 and 6, respectively.

Inspec keywords: target tracking; sampling methods; Bayes methods; filtering theory; Gaussian processes

Other keywords: general MS Poisson MB filter; multitarget tracking; MB mixture; single-sensor Poisson multiBernoulli mixture filter; PMBM conjugate posterior; PMBM filter; multisensor Poisson multiBernoulli filter; MS-MTT; multisensor extensions; greedy measurement partition algorithm; partitioned measurements; MS measurement likelihood; single MB

Subjects: Other topics in statistics; Filtering methods in signal processing; Signal processing theory; Other topics in statistics

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