Multiple Object Trajectory Estimation Using Backward Simulation
Journal article, 2022

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.

backward simulation


Density measurement

Mathematical models

Electrical engineering

random finite sets

Probabilistic logic

Multi-object tracking

forward-backward smoothing

sets of trajectories

Smoothing methods

Signal processing algorithms


Yuxuan Xia

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Lennart Svensson

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Angel F. Garcia-Fernandez

University of Liverpool

Jason L. Williams

Commonwealth Scientific and Industrial Research Organisation (CSIRO)

Daniel Svensson


Karl Granström

Embark Trucks Inc.

IEEE Transactions on Signal Processing

1053-587X (ISSN) 1941-0476 (eISSN)

Vol. 70 3249-3263

Subject Categories

Probability Theory and Statistics

Signal Processing

Computer Vision and Robotics (Autonomous Systems)



More information

Latest update