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A Task Priority-Driven Resource Scheduling Method of Asynchronous Phased Array Radar Network for Cognitive Tracking in Clutter
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摘要: 相控阵雷达网络在复杂环境下的多目标跟踪能力受到有限资源与异步采样机制的双重制约，尤其在跟踪与搜索(TAS)体制下，搜索与跟踪任务之间的资源竞争以及量测不确定性会显著影响系统整体跟踪性能。针对上述问题，本文提出一种杂波环境下任务优先级驱动的异步相控阵雷达网络(APARN)认知跟踪资源调度方法(TPRS)。该方法在资源调度模型中引入软关联概率以及虚警密度、检测概率等环境参数，刻画量测不确定性对资源调度决策的影响；并在TAS体制下，针对目标已通过多帧检测完成航迹起始的场景，将资源调度重点聚焦于跟踪任务，在资源受限条件下按优先级实施序贯调度。在此基础上，结合集中式资源调度与分布式状态估计融合构建APARN闭环多目标跟踪框架，通过联合概率数据关联(JPDA)实现多目标状态估计，利用协方差交集(CI)完成异步量测融合，并以引入关联不确定性的后验Cramér–Rao下界(PCRLB)作为调度性能评价指标。针对该优化问题多维决策变量耦合且属于NP难问题的特点，提出一种多维解耦与顺序动态规划相结合的两阶段求解方法，以降低计算复杂度并实现资源受限条件下雷达-目标动态匹配与异步驻留时间的自适应调度。仿真结果表明，在资源受限与杂波干扰条件下，所提方法能够有效提升APARN的整体多目标跟踪精度与资源利用效率，为异步多雷达协同跟踪系统的工程化应用提供了可行技术路径。Abstract: The multi-target tracking performance of phased array radar networks is fundamentally constrained by limited resources and asynchronous sampling mechanisms, especially under the Track and Search (TAS) paradigm, where competition between search and tracking tasks, along with measurement uncertainty, significantly affects overall system performance. To address these challenges, this paper proposes a task-priority-driven cognitive tracking resource scheduling method for asynchronous phased array radar networks (APARN), termed TPRS, in cluttered environments. The proposed method incorporates soft association probabilities, as well as environmental parameters such as false alarm density and detection probability, into the resource scheduling model to characterize the impact of measurement uncertainty on decision-making. Under the TAS framework, considering scenarios where target tracks have been initiated through multi-frame detections, the scheduling strategy emphasizes tracking tasks and performs priority-driven sequential allocation under resource constraints. On this basis, a closed-loop multi-target tracking framework for APARN is developed by integrating centralized resource scheduling with distributed state estimation and fusion. Joint probabilistic data association (JPDA) is employed for multi-target state estimation, while covariance intersection (CI) is adopted for asynchronous measurement fusion. The posterior Cramér–Rao lower bound (PCRLB), incorporating association uncertainty, is used as the performance metric for scheduling. Given that the resulting optimization problem involves coupled multi-dimensional decision variables and is NP-hard, a two-stage solution method combining multi-dimensional decoupling and sequential dynamic programming is proposed to reduce computational complexity and enable adaptive scheduling of radar–target assignment and asynchronous dwell time under resource constraints. Simulation results demonstrate that, under limited resources and clutter interference, the proposed method effectively improves overall multi-target tracking accuracy and resource utilization efficiency in APARN, providing a feasible technical pathway for practical deployment of asynchronous multi-radar cooperative tracking systems.
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图 1 异步系统结构及关键参数说明图例
Figure 1. Asynchronous System Architecture and Illustration of Key Parameter Descriptions
图 4 APARN在MTT过程中的单次雷达-目标分配结果
Figure 4. Single Radar-Target Assignment Results of APARN During MTT
图 5 APARN在MTT过程中单次的驻留时间分配结果
Figure 5. Single Dwell Time Allocation Results of APARN During MTT
图 7 TPRS各目标关联成功概率曲线
Figure 7. Curves of successful association probability for each target in TPRS
表 1 PAR位置和采样周期
Table 1. PAR positions and sampling periods
PAR1 PAR2 PAR3 位置 (–20, 25) (–20, 50) (–20, 75) 采样周期(s) 6 3 2 
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表 2 目标初始状态
Table 2. Initial states of targets
目标1 目标2 目标3 目标4 目标5 目标6 位置(Km) (150, 0) (150, 25) (130, 55) (150, 45) (150, 75) (150, 100) 速度(m/s) (–700, 100) (–500, 100) (–500, 0) (–700, 0) (–500, –100) (–700, –200)
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表 3 关键参数
Table 3. Key Parameters
参数名称 过程噪声强度$ \gamma $ 有效带宽 平均辐射功率$ {P}_{\text{av}} $ 虚警密度 验证门参数 检测概率 杂波类型 RCS 参数值 0.012 106 Hz 104 W> 1e–6 3.5 0.95 空间均匀 10
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