基于交互式CPHD的多传感器多机动目标跟踪
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桂林电子科技大学

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中图分类号:

TP391

基金项目:

国家自然科学基金项目(11661024,61861008);广西自然科学基金(2016GXNSFAA380073);广西研究生教育创新计划项目(2020YCXS084)


Multi-sensor and Multi-maneuver target tracking based on interactive CPHD
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Affiliation:

Guilin University of Electronic Technology

Fund Project:

The National Natural Science Foundation of China (No.11661024,No.61861008); Guangxi Natural Science Foundation (No.2016GXNSFAA380073); Innovation Project of Guangxi Graduate Education(No.2020YCXS084)

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    摘要:

    针对多传感器高速多机动目标的跟踪问题,提出了一种多传感器交互式贪婪势概率假设密度(MS-IMM-Greedy-CPHD) 滤波器.该滤波器在预测阶段,通过交互式多模(IMM)算法对CPHD滤波中目标的状态、势分布和运动模型同时进行预测;在滤波的更新阶段,利用贪婪(Greedy) 量测划分机制选取多传感器量测子集和拟分区,并通过拟分区量测子集对不同模型下CPHD 预测的目标状态和势分布以及模型进行交互式更新.仿真结果表明,所提MS-IMM-Greedy-CPHD 滤波能够对高机动多目标进行稳定有效的跟踪,相较于多传感器势概率假设密度(MS-CPHD)滤波,本文方法跟踪结果的OSPA 误差更小且势估计更加准确.

    Abstract:

    Aiming at the tracking problem of multi-sensor high-speed and multiple maneuvering targets, a multi-sensor interactive greedy cardinalized probability hypothesis density (MS-IMM-Greedy-CPHD) filter is proposed. In the prediction stage, the interacting multi-mode (IMM) algorithm is used to predict the state, potential distribution and motion model of the target in CPHD filtering; in the update stage of the filter, the greedy measurement partition strategy is used to select the multi-sensor measurement subsets and quasi-partition regions, and the quasi-partition measurement subset is used to predict the target state and potential distribution under different models which is updated interactively. Simulation results show that the proposed MS-IMM-Greedy-CPHD filter can track high maneuvering multi-target stably and effectively. Compared with the multi-sensor cardinalized probability hypothesis density (MS-CPHD) filter, the OSPA error of the proposed method is smaller and the cardinalized estimation is more accurate.

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历史
  • 收稿日期:2020-07-17
  • 最后修改日期:2021-09-14
  • 录用日期:2020-10-12
  • 在线发布日期: 2020-12-01
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