An Efficient Implementation of the Extended Object Trajectory PMB Filter Using Blocked Gibbs Sampling
Paper i proceeding, 2023

This paper presents an efficient implementation of the trajectory Poisson multi-Bernoulli (PMB) filter for multiple extended object tracking (EOT), which directly estimates a set of object trajectories. The trajectory PMB filter propagates a PMB density on the posterior of sets of trajectories through the filtering recursions over time, where the multi-Bernoulli (MB) mixture in the PMB mixture (PMBM) posterior after each update step is approximated as a single MB. The efficient MB approximation is achieved by first running a blocked Gibbs sampler on the joint posterior of the set of trajectories and the measurement association variables. The single-object measurement model is assumed to be a Poisson point process which enables us to parallelize the sampling across all objects and association variables, respectively. Then, samples of object states are utilized to form the approximate MB density via Kullback-Leibler divergence minimization. Simulation results on EOT with known and constant elliptical shapes show that the TPMB implementation using blocked Gibbs sampling outperforms the state-of-the-art TPMB implementation using loopy belief propagation with significantly reduced runtime.

random finite sets

sets of trajectories

Multi-object tracking

extended object tracking

Gibbs sampling

Författare

Yuxuan Xia

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Angel Garcia

Universidad Nebrija

University of Liverpool

Lennart Svensson

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

2023 26th International Conference on Information Fusion, FUSION 2023


9798890344854 (ISBN)

26th International Conference on Information Fusion, FUSION 2023
Charleston, USA,

Ämneskategorier

Sannolikhetsteori och statistik

Reglerteknik

Signalbehandling

DOI

10.23919/FUSION52260.2023.10224190

Mer information

Senast uppdaterat

2023-09-29