Multiple Object Trajectory Estimation Using Backward Simulation
Artikel i vetenskaplig tidskrift, 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
Trajectory
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
Författare
Yuxuan Xia
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Lennart Svensson
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Angel F. Garcia-Fernandez
University of Liverpool
Jason L. Williams
Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Daniel Svensson
NVIDIA
Karl Granström
Embark Trucks Inc.
IEEE Transactions on Signal Processing
1053-587X (ISSN) 1941-0476 (eISSN)
Vol. 70 3249-3263Ämneskategorier
Sannolikhetsteori och statistik
Signalbehandling
Datorseende och robotik (autonoma system)
DOI
10.1109/TSP.2022.3184794