A fast implementation of the Labeled Multi-Bernoulli filter using gibbs sampling
Paper in proceedings, 2017

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.


S. Reuter

University of Ulm

A. Danzer

University of Ulm

M. Stubler

University of Ulm

A. Scheel

University of Ulm

Karl Granström

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

28th IEEE Intelligent Vehicles Symposium, IV 2017, Redondo Beach, United States, 11-14 June 2017


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Signal Processing





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