Due to the requirement of anti-interception and the limitation of processing capability of the fusion center, the subarray selection is very important for the distributed multiple-input multiple-output (MIMO) radar system, especially in the hostile environment. In such conditions, an efficient subarray selection strategy is proposed for MIMO radar performing tasks of target tracking and detection. The goal of the proposed strategy is to minimize the worst-case predicted posterior Cramer-Rao lower bound (PCRLB) while maximizing the detection probability for a certain region. It is shown that the subarray selection problem is NP-hard, and a modified particle swarm optimization (MPSO) algorithm is developed as the solution strategy. A large number of simulations verify that the MPSO can provide close performance to the exhaustive search (ES) algorithm. Furthermore, the MPSO has the advantages of simpler structure and lower computational complexity than the multi-start local search algorithm.

中文翻译：

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分布式MIMO雷达联合目标跟踪检测的有限传感器选择算法

由于抗拦截的要求和融合中心处理能力的限制，子阵选择对于分布式多输入多输出（MIMO）雷达系统非常重要，尤其是在恶劣环境中。在这种情况下，提出了一种有效的子阵列选择策略，用于 MIMO 雷达执行目标跟踪和检测任务。所提出策略的目标是最小化最坏情况预测后验 Cramer-Rao 下界 (PCRLB)，同时最大化某个区域的检测概率。结果表明，子阵列选择问题是 NP-hard 问题，改进的粒子群优化 (MPSO) 算法被开发为解决策略。大量仿真验证了 MPSO 可以提供与穷举搜索 (ES) 算法相近的性能。此外，MPSO 与多起始局部搜索算法相比，具有结构更简单、计算复杂度更低的优点。