Dynamic Antenna Selection for Colocated MIMO Radar
Dynamic Antenna Selection for Colocated MIMO Radar
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DOI：
10.3390/rs14122912
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Gangsheng Zhang;Jun-wei Xie;Hao-wei Zhang;Zhengjie Li;Cheng Qi
中科院分区：
文献类型：
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作者：
Gangsheng Zhang;Jun-wei Xie;Hao-wei Zhang;Zhengjie Li;Cheng Qi
在多输入多输出（MIMO）雷达目标跟踪中，天线分布对雷达的性能增益起着重要作用。由于要满足低截获概率的要求，特别是在恶劣的环境中，每一步只能激活有限数量的天线。这自然会导致性能驱动的资源管理问题。提出了一种用于MIMO雷达目标跟踪的动态天线选择策略。推导出的波达方向和多普勒联合估计的后验Cramér-Rao下界（PCRLB）作为优化准则。此外，在偏差中，目标雷达散射截面（RCS）作为决定变量和随机变量都进行了讨论。该目标函数与天线配置有关且为非凸函数，提出了一种高效的快速离散粒子群优化算法（FDPSO）进行解搜索。此外，建立了一个闭环反馈系统，其中的主要思想是，从当前的时间历元的跟踪信息被用来预测PCRLB，并指导下一个时间历元的天线调整。仿真结果表明，与三种固定天线配置相比，性能有所改善。此外，FDPSO可以提供接近最优的解决方案，同时满足实时性的要求。
Antenna distribution plays an important role for the performance gain in multiple-input–multiple-output (MIMO) radar target tracking. Since to meet the requirements of the low probability of interception, especially in a hostile environment, only a finite number of antennas can be activated at each step. This naturally leads to a performance-driven resource management problem. In this paper, a dynamic antenna selection strategy is proposed for tracking targets in colocated MIMO radar. The derived posterior Cramér–Rao lower bound (PCRLB) of joint direction-of-arrival (DOA) and Doppler estimate were chosen as the optimization criteria. Furthermore, in the deviation, the target radar cross-section (RCS) as the determining variable and the random variable are both discussed. The objective function is related to the antenna allocation and non-convex, and an efficient fast discrete particle swarm optimization (FDPSO) algorithm is proposed for the solution exploration. Additionally, a closed-loop feedback system is established, where the main idea is that the tracking information from the current time epoch is utilized to predict the PCRLB and to guide the antenna adjustment for the next time epoch. The simulation results demonstrate the performance improvement compared with the three fixed-antenna configurations. Moreover, the FDPSO can provide close-to-optimal solutions while satisfying the real-time demand.