A Poisson multi-Bernoulli mixture filter for coexisting point and extended targets
Abstract
This paper proposes a Poisson multi-Bernoulli mixture (PMBM) filter for coexisting point and extended targets, i.e., for scenarios where there may be simultaneous point and extended targets. The PMBM filter provides a recursion to compute the multi-target filtering posterior based on probabilistic information on data associations, and single-target predictions and updates. In this paper, we first derive the PMBM filter update for a generalised measurement model, which can include measurements originated from point and extended targets. Second, we propose a single-target space that accommodates both point and extended targets and derive the filtering recursion that propagates Gaussian densities for point targets and gamma Gaussian inverse Wishart densities for extended targets. As a computationally efficient approximation of the PMBM filter, we also develop a Poisson multi-Bernoulli (PMB) filter for coexisting point and extended targets. The resulting filters are analysed via numerical simulations.
Cite
@article{arxiv.2011.04464,
title = {A Poisson multi-Bernoulli mixture filter for coexisting point and extended targets},
author = {Ángel F. García-Fernández and Jason L. Williams and Lennart Svensson and Yuxuan Xia},
journal= {arXiv preprint arXiv:2011.04464},
year = {2021}
}
Comments
Matlab files can be found at https://github.com/Agarciafernandez/Coexisting-point-extended-target-PMBM-filter and https://github.com/yuhsuansia/Coexisting-point-extended-target-PMBM-filter. A relevant multi-object tracking course can be found at https://www.youtube.com/channel/UCa2-fpj6AV8T6JK1uTRuFpw