Multiple Object Trajectory Estimation Using Backward Simulation

Xia, Yuxuan, Svensson, Lennart, Garcia-Fernandez, Angel ORCID: 0000-0002-6471-8455, L. Williams, Jason, Svensson, Daniel and Granstrom, Karl (2022) Multiple Object Trajectory Estimation Using Backward Simulation. IEEE Transactions on Signal Processing, 70. pp. 1-15.

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Official URL: http://dx.doi.org/10.1109/tsp.2022.3184794

Abstract

This paper presents a general solution for computing the multi-object posterior for sets of trajectories from a sequence of multi-object (unlabelled) filtering densities and a multi-object dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multi-object filters that do not explicitly estimate trajectories. In this paper, we first derive a general multi-trajectory backward smoothing equation based on random finite sets of trajectories. Then we show how to sample sets of trajectories using backward simulation for Poisson multi-Bernoulli filtering densities, and develop a tractable implementation based on ranked assignment. The performance of the resulting multi-trajectory particle smoothers is evaluated in a simulation study, and the results demonstrate that they have superior performance in comparison to several state-of-the-art multi-object filters and smoothers.

Item Type:	Article
Uncontrolled Keywords:	Trajectory, Mathematical models, Smoothing methods, Signal processing algorithms, Probabilistic logic, Electrical engineering, Density measurement, Multi-object tracking, random finite sets, sets of trajectories, forward-backward smoothing, backward simulation
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	23 Jun 2022 07:44
Last Modified:	17 Mar 2024 14:31
DOI:	10.1109/tsp.2022.3184794
Open Access URL:	https://arxiv.org/abs/2206.08112
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3157029