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A fast implementation of the Labeled Multi-Bernoulli filter using gibbs sampling

S. Reuter ; A. Danzer ; M. Stubler ; A. Scheel ; Karl Granström (Institutionen för signaler och system, Signalbehandling)
28th IEEE Intelligent Vehicles Symposium, IV 2017, Redondo Beach, United States, 11-14 June 2017 p. 765-772. (2017)
[Konferensbidrag, refereegranskat]

This paper proposes a fast implementation of the Labeled Multi-Bernoulli (LMB) filter based on a joint prediction and update scheme. The joint calculation prevents the treatment of insignificant hypotheses, e.g. considering the disappearance of an object with high existence probability which additionally generated a precise measurement in the received measurement set. Further, a Gibbs sampling approach for generating association hypotheses is presented which drastically reduces the computational complexity compared to Murtys ranked-Assignment algorithm. The proposed Gibbs sampling implementation is compared to the standard implementation of the LMB filter using two scenarios: Tracking vehicles using a multi-sensor setup on a German highway and extended object tracking in an urban scenario using Velodyne data.

Denna post skapades 2017-09-12.
CPL Pubid: 251826


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Institutioner (Chalmers)

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



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