Gaussian implementation of the multi-Bernoulli mixture filter
Paper in proceedings, 2019

This paper presents the Gaussian implementation of the multi-Bernoulli mixture (MBM) filter. The MBM filter provides the filtering (multi-target) density for the standard dynamic and radar measurement models when the birth model is multi-Bernoulli or multi-Bernoulli mixture. Under linear/Gaussian models, the single target densities of the MBM mixture admit Gaussian closed-form expressions. Murty's algorithm is used to select the global hypotheses with highest weights. The MBM filter is compared with other algorithms in the literature via numerical simulations.

multi-target conjugate priors

Poisson multi-Bernoulli mixtures

Multiple target tracking

Author

Angel F. Garcaa-Fernandez

University of Liverpool

Yuxuan Xia

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Karl Granström

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Lennart Svensson

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Jason L. Williams

Commonwealth Scientific and Industrial Research Organisation (CSIRO)

FUSION 2019 - 22nd International Conference on Information Fusion

9011346

22nd International Conference on Information Fusion, FUSION 2019
Ottawa, Canada,

Subject Categories

Probability Theory and Statistics

Control Engineering

Signal Processing

More information

Latest update

10/29/2020