Backward simulation for sets of trajectories
Paper in proceedings, 2020

This paper presents a solution for recovering full trajectory information, via the calculation of the posterior of the set of trajectories, from a sequence of multitarget (unlabelled) filtering densities and the multitarget dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multitarget filters that do not explicitly estimate trajectories. In this paper, we first derive a general multitrajectory forward-backward smoothing equation based on sets of trajectories and the random finite set framework. Then we show how to sample sets of trajectories using backward simulation when the multitarget filtering densities are multi-Bernoulli processes. The proposed approach is demonstrated in a simulation study.

sets of trajectories

Multitarget smoothing

backward simulation

forward-backward smoothing

Author

Yuxuan Xia

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

Lennart Svensson

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

Angel Garcia

University of Liverpool

Karl Granström

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

Jason L. Williams

Commonwealth Scientific and Industrial Research Organisation (CSIRO)

Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020

9190164

2020 IEEE 23rd International Conference on Information Fusion (FUSION)
Rustenburg, South Africa,

Deep multi-object tracking for ground truth trajectory estimation

VINNOVA, 2018-07-01 -- 2022-06-30.

Subject Categories

Computational Mathematics

Probability Theory and Statistics

Signal Processing

DOI

10.23919/FUSION45008.2020.9190164

More information

Latest update

12/23/2020