多雷达协同探测技术研究进展:认知跟踪与资源调度算法

易伟 袁野 刘光宏 葛建军 孔令讲 杨建宇

易伟, 袁野, 刘光宏, 等. 多雷达协同探测技术研究进展:认知跟踪与资源调度算法[J]. 雷达学报, 2023, 12(3): 471–499. doi: 10.12000/JR23036
引用本文: 易伟, 袁野, 刘光宏, 等. 多雷达协同探测技术研究进展:认知跟踪与资源调度算法[J]. 雷达学报, 2023, 12(3): 471–499. doi: 10.12000/JR23036
YI Wei, YUAN Ye, LIU Guanghong, et al. Recent advances in multi-radar collaborative surveillance: Cognitive tracking and resource scheduling algorithms[J]. Journal of Radars, 2023, 12(3): 471–499. doi: 10.12000/JR23036
Citation: YI Wei, YUAN Ye, LIU Guanghong, et al. Recent advances in multi-radar collaborative surveillance: Cognitive tracking and resource scheduling algorithms[J]. Journal of Radars, 2023, 12(3): 471–499. doi: 10.12000/JR23036

多雷达协同探测技术研究进展:认知跟踪与资源调度算法

doi: 10.12000/JR23036
基金项目: 国家自然科学基金(62231008, U19B2017),中央高校基本科研业务费专项资金(ZYGX2020ZB029)
详细信息
    作者简介:

    易 伟,博士,教授,研究方向为低可观测目标检测跟踪、多雷达协同探测等

    袁 野,博士,博士后,研究方向为多雷达协同探测、雷达资源管控技术等

    刘光宏,博士,研究员,研究方向为雷达总体设计、智能协同感知技术

    葛建军,博士,电科集团首席科学家,研究方向为雷达探测技术、认知与智能技术

    孔令讲,博士,教授,研究方向为雷达信号处理、新体制雷达、统计信号处理等

    杨建宇,博士,教授,研究方向为雷达信号处理、合成孔径雷达成像等

    通讯作者:

    易伟 kusso@uestc.edu.cn

  • 责任主编:丁建江 Corresponding Editor: DING Jianjiang
  • 中图分类号: TN95

Recent Advances in Multi-radar Collaborative Surveillance: Cognitive Tracking and Resource Scheduling Algorithms

Funds: The National Natural Science Foundation of China (62231008, U19B2017), The Fundamental Research Funds for the Central Universities (ZYGX2020ZB029)
More Information
  • 摘要: 多雷达协同探测技术通过有机地联动多部雷达,形成广域分布的探测构型,可充分获取空间、频率分集等探测增益,显著提升雷达系统的目标探测性能和电磁干扰环境顽存能力,是雷达技术领域重点发展的方向之一。近年来,国内外针对多雷达协同探测技术开展了广泛研究,在系统架构设计、信号处理、资源调度等技术方向积累了诸多研究成果。该文首先总结了多雷达协同探测技术的概念内涵,阐述了其基于信号处理闭环反馈的协同机制,分析了其实现过程中所面临的技术挑战;随后,聚焦于认知跟踪与资源调度算法,从内涵特点、系统构成、跟踪模型、信息融合、性能评估、调度算法、优化准则、认知流程等方面进行了技术总结,并分析了协同认知跟踪及其与系统资源调度的关系;接着从雷达资源要素、信息融合架构、跟踪性能指标、资源调度模型、复杂任务场景5个方面梳理和总结了协同认知跟踪与资源调度算法近年来的研究进展;最后总结全文并展望了该领域未来技术的发展趋势,旨在为后续的相关技术研究提供参考。

     

  • 图  1  复杂电磁环境下多雷达协同对空探测示意图

    Figure  1.  Schematic diagram of multi-radar collaborative air surveillance in complex electromagnetic environment

    图  2  多雷达协同探测示意图

    Figure  2.  Schematic diagram of multi-radar collaborative surveillance

    图  3  多雷达协同认知闭环构建

    Figure  3.  Cognitive closed-loop for multi-radar collaborative surveillance

    图  4  多雷达协同探测的信号处理认知闭环

    Figure  4.  Cognitive closed-loop for the signal processing of multi-radar collaborative surveillance

    图  5  多雷达协同探测及资源调度算法处理流程的认知闭环

    Figure  5.  Cognitive closed-loop for the processing of multi-radar collaborative surveillance and resource allocation algorithms

    图  6  认知跟踪算法与传统融合跟踪算法的区别

    Figure  6.  The difference between traditional target tracking and recognitive tracking algorithms

    图  7  多雷达协同多目标跟踪场景

    Figure  7.  Scenario of multi-radar collaborative target tracking

    图  8  网络化MIMO雷达多目标跟踪场景意图

    Figure  8.  Scenario of multi-target tracking with netted MIMO radar

    图  9  多雷达协同信息融合架构

    Figure  9.  Multi-radar information fusion architectures

    图  10  资源调度为基础的雷达认知跟踪闭环的处理流程

    Figure  10.  Resource allocation-based processing flow of the radar cognitive tracking

    图  11  3类可能的目标状态估计结果[126]

    Figure  11.  Three possible results of target state estimation[126]

    图  12  网络化MIMO雷达多目标跟踪场景[23]

    Figure  12.  Multi-target tracking scenario using netter MIMO radar[23]

    图  13  MinMax和QoS模型下多目标跟踪性能对比[23]

    Figure  13.  Multi-target tracking performance comparison between MinMax and QoS models[23]

    图  14  MinMax和QoS模型下资源分配结果对比(归一化发射功率)[23]

    Figure  14.  Resource allocation comparison between MinMax and QoS models (normalized transmit power)[23]

    图  15  直接资源最小化和QoS模型下多目标跟踪性能对比[33]

    Figure  15.  Multi-target tracking performance comparison between Direct resoure minimization and QoS models[33]

    表  1  文献[23]中QoS模型3种不同参数设置

    Table  1.   The 3 different parameters setting for the QoS model in Ref. [23]

    参数 $\left[ {\eta _k^1,\eta _k^2,\eta _k^3,\eta _k^4} \right]$$\left[ {{\varpi ^1},{\varpi ^2},{\varpi ^3},{\varpi ^4}} \right]$
    参数设置1[60 m, 60 m, 120 m, 240 m][0.25, 0.25, 0.25, 0.25]
    参数设置2[60 m, 60 m, 60 m, 240 m][0.25, 0.25, 0.25, 0.25]
    参数设置3[60 m, 60 m, 60 m, 240 m][0.40, 0.10, 0.40, 0.10]
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  • 收稿日期:  2023-03-20
  • 修回日期:  2023-06-09
  • 网络出版日期:  2023-06-27
  • 刊出日期:  2023-06-28

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