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Poisson Multi-Bernoulli Radar Mapping Using Gibbs Sampling

Maryam Fatemi (Institutionen för signaler och system, Signalbehandling) ; Karl Granström (Institutionen för signaler och system, Signalbehandling) ; Lennart Svensson (Institutionen för signaler och system, Signalbehandling) ; Francisco J. R. Ruiz ; Lars Hammarstrand (Institutionen för signaler och system, Signalbehandling)

This paper addresses the radar mapping problem. Using a conjugate prior form, we derive the exact theoretical batch multi-object posterior density of the map given a set of measurements. The landmarks in the map are modeled as extended objects, and the measurements are described as a Poisson process, conditioned on the map. We use a Poisson process prior on the map and prove that the posterior distribution is a hybrid Poisson, multi-Bernoulli mixture distribution. We devise a Gibbs sampling algorithm to sample from the batch multi-object posterior. The proposed method can handle uncertainties in the data associations and the cardinality of the set of landmarks, and is parallelizable, making it suitable for large-scale problems. The performance of the proposed method is evaluated on synthetic data and is shown to outperform an state-of-the-art method.

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Denna post skapades 2016-08-09. Senast ändrad 2017-01-27.
CPL Pubid: 239984


Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling (1900-2017)



Chalmers infrastruktur

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