CLC number: TN953
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2023-10-04
Cited: 0
Clicked: 1515
Qiang GUO, Long TENG, Tianxiang YIN, Yunfei GUO, Xinliang WU, Wenming SONG. Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(11): 1647-1656.
@article{title="Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking",
author="Qiang GUO, Long TENG, Tianxiang YIN, Yunfei GUO, Xinliang WU, Wenming SONG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="11",
pages="1647-1656",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300348"
}
%0 Journal Article
%T Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking
%A Qiang GUO
%A Long TENG
%A Tianxiang YIN
%A Yunfei GUO
%A Xinliang WU
%A Wenming SONG
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 11
%P 1647-1656
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300348
TY - JOUR
T1 - Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking
A1 - Qiang GUO
A1 - Long TENG
A1 - Tianxiang YIN
A1 - Yunfei GUO
A1 - Xinliang WU
A1 - Wenming SONG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 11
SP - 1647
EP - 1656
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2300348
Abstract: The performance of existing maneuvering target tracking methods for highly maneuvering targets in cluttered environments is unsatisfactory. This paper proposes a hybrid-driven approach for tracking multiple highly maneuvering targets, leveraging the advantages of both data-driven and model-based algorithms. The time-varying constant velocity model is integrated into the gaussian process (GP) of online learning to improve the performance of GP prediction. This integration is further combined with a generalized probabilistic data association algorithm to realize multi-target tracking. Through the simulations, it has been demonstrated that the hybrid-driven approach exhibits significant performance improvements in comparison with widely used algorithms such as the interactive multi-model method and the data-driven GP motion tracker.
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