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.
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.
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Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Qiang GUO and Long TENG designed the research and addressed the problems. Long TENG processed the data and drafted the paper. Yunfei GUO and Tianxiang YIN helped with the technical information. Xinliang WU and Wenming SONG helped organize the paper and supervised the study. Long TENG revised and finalized the paper.
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Qiang GUO, Long TENG, Tianxiang YIN, Yunfei GUO, Xinliang WU, and Wenming SONG declare that they have no conflict of interest.
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Project supported by the Technology Foundation for Basic Enhancement Plan, China (No. 2021-JCJQ-JJ-0301), the National Major Research and Development Project of China (No. 2018YFE0206500), the National Natural Science Foundation of China (No. 62071140), and the National Special for International Scientific and Technological Cooperation of China (No. 2015DFR10220)
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Guo, Q., Teng, L., Yin, T. et al. Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking. Front Inform Technol Electron Eng 24, 1647–1656 (2023). https://doi.org/10.1631/FITEE.2300348
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DOI: https://doi.org/10.1631/FITEE.2300348