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Poisson multi-Bernoulli conjugate prior for estimation of both detected and undetected extended objects

Karl Granström (Institutionen för signaler och system, Signalbehandling) ; Maryam Fatemi (Institutionen för signaler och system, Signalbehandling) ; Lennart Svensson (Institutionen för signaler och system, Signalbehandling)

This paper presents a Poisson multi-Bernoulli mixture (PMBM) conjugate prior for multiple extended object estimation. A Poisson point process is used to describe the existence of yet undetected targets, while a multi-Bernoulli mixture describes the distribution of the targets that have been detected. The prediction and update equations are presented for the standard transition density and measurement likelihood. Both the prediction and the update preserve the PMBM form of the density, and in this sense the PMBM density is a conjugate prior. However, the unknown data associations lead to an intractably large number of terms in the PMBM density, and approximations are necessary for tractability. A gamma Gaussian inverse Wishart implementation is presented, along with methods to handle the data association problem. A simulation study shows that the extended target PMBM filter outperforms the extended target PHD, CPHD and LMB filters.

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Denna post skapades 2016-08-09.
CPL Pubid: 239983


Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling



Chalmers infrastruktur

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