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Recent Advances in Multi-radar Collaborative Surveillance: Cognitive Tracking and Resource Scheduling Algorithms
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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。Abstract: Multi-Radar Collaborative Surveillance (MRCS) technology enables a geographically distributed detection configuration through the linkage of multiple radars, which can fully obtain detection gains in terms of spatial and frequency diversity, thereby enhancing the detection performance and viability of radar systems in the context of complex electromagnetic environments. MRCS is one of the key development directions in radar technology and has received extensive attention in recent years. Considerable research on MRCS has been conducted, and numerous achievements in system architecture design, signal processing, and resource scheduling for MRCS have been accumulated. This paper first summarizes the concept of MRCS technology, elaborates on the signal processing-based closed-loop mechanism of cognitive collaboration, and analyzes the challenges faced in the process of MRCS’s implementation. Then, the paper focuses on cognitive tracking and resource scheduling algorithms and implements the technical summary regarding the connotation characteristics, system configuration, tracking model, information fusion, performance evaluation, resource scheduling algorithm, optimization criteria, and cognitive process of cognitive tracking. The relevance between multi-radar cognitive tracking and its system resource scheduling is further analyzed. Subsequently, the recent research trends of cognitive tracking and resource scheduling algorithms are identified and summarized in terms of five aspects: radar resource elements, information fusion architectures, tracking performance indicators, resource scheduling models, and complex task scenarios. Finally, the full text is summarized and future technology in this field is explored to provide a reference for subsequent research on related technologies.
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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 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 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 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
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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