Full Text:   <579>

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CLC number: TN953

On-line Access: 2023-12-04

Received: 2023-05-18

Revision Accepted: 2023-12-05

Crosschecked: 2023-10-04

Cited: 0

Clicked: 460

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Long TENG

https://orcid.org/0000-0003-3519-7790

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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.11 P.1647-1656

http://doi.org/10.1631/FITEE.2300348


Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking


Author(s):  Qiang GUO, Long TENG, Tianxiang YIN, Yunfei GUO, Xinliang WU, Wenming SONG

Affiliation(s):  College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China; more

Corresponding email(s):   guoqiang@hrbeu.edu.cn, tenglong@hrbeu.edu.cn, tianxiangyin@hdu.edu.cn, gyf@hdu.edu.cn

Key Words:  Target tracking, Gaussian process, Data-driven, Online learning, Model-driven, Probabilistic data association


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.

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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"
}

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T1 - Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking
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A1 - Wenming SONG
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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.

基于混合驱动高斯过程学习的强机动多目标跟踪方法

国强1,滕龙1,2,尹天祥3,郭云飞3,吴新良2,宋文明2
1哈尔滨工程大学信息与通信工程学院,中国哈尔滨市,150001
2中国航空无线电电子研究所,中国上海市,200233
3杭州电子科技大学自动化学院,中国杭州市,310018
摘要:现有机动目标跟踪方法在杂波环境中强机动目标的跟踪性能并不令人满意。本文提出一种混合驱动方法,利用数据驱动和基于模型算法的优点跟踪多个高机动目标。将时变恒速(CV)模型集成到在线学习的高斯过程(GP)中,提高高斯过程的预测性能。进一步与广义概率数据关联(GPDA)算法相结合,实现多目标跟踪。通过仿真实验可知,与广泛使用的机动目标跟踪算法如交互式多模型(IMM)和数据驱动的高斯过程运动跟踪器(GPMT)相比,提出的混合驱动方法具有显著的性能优势。

关键词:目标跟踪;高斯过程;数据驱动;在线学习;模型驱动;概率数据关联

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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