Collaborative Trajectory Planning and Resource Allocation for Multi-Target Tracking in Airborne Radar Networks under Spectral Coexistence

Remote. Sens. Pub Date : 2023-07-03 DOI:10.3390/rs15133386
C. Shi, Jing Dong, S. Salous, Ziwei Wang, Jianjiang Zhou
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Abstract

This paper develops a collaborative trajectory planning and resource allocation (CTPRA) strategy for multi-target tracking (MTT) in a spectral coexistence environment utilizing airborne radar networks. The key mechanism of the proposed strategy is to jointly design the flight trajectory and optimize the radar assignment, transmit power, dwell time, and signal effective bandwidth allocation of multiple airborne radars, aiming to enhance the MTT performance under the constraints of the tolerable threshold of interference energy, platform kinematic limitations, and given illumination resource budgets. The closed-form expression for the Bayesian Cramér–Rao lower bound (BCRLB) under the consideration of spectral coexistence is calculated and adopted as the optimization criterion of the CTPRA strategy. It is shown that the formulated CTPRA problem is a mixed-integer programming, non-linear, non-convex optimization model owing to its highly coupled Boolean and continuous parameters. By incorporating semi-definite programming (SDP), particle swarm optimization (PSO), and the cyclic minimization technique, an iterative four-stage solution methodology is proposed to tackle the formulated optimization problem efficiently. The numerical results validate the effectiveness and the MTT performance improvement of the proposed CTPRA strategy in comparison with other benchmarks.
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频谱共存条件下机载雷达网络多目标跟踪协同轨迹规划与资源分配
提出了一种基于机载雷达网络的频谱共存环境下多目标跟踪协同轨迹规划与资源分配策略。该策略的关键机制是共同设计飞行轨迹,优化多部机载雷达的雷达配置、发射功率、停留时间和信号有效带宽分配,在干扰能量可容忍阈值、平台运动学限制和给定照明资源预算的约束下提高MTT性能。计算了考虑谱共存条件下Bayesian cram - rao下界(BCRLB)的封闭表达式,并将其作为CTPRA策略的优化准则。结果表明,所构造的CTPRA问题是一个混合整数规划、非线性、非凸优化模型,具有布尔参数和连续参数的高度耦合。结合半确定规划(SDP)、粒子群优化(PSO)和循环最小化技术,提出了一种四阶段迭代求解方法,有效地解决了公式化优化问题。数值结果验证了CTPRA策略的有效性和MTT性能的提高。
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