At present, multiple input multiple output radars offer accurate target detection and better target parameter estimation with higher resolution in high‐speed wireless communication systems. This study focuses primarily on power allocation to improve the performance of radars owing to the sparsity of targets in the spatial velocity domain. First, the radars are clustered using the kernel fuzzy C‐means algorithm. Next, cooperative and noncooperative clusters are extracted based on the distance measured using the kernel fuzzy C‐means algorithm. The power is allocated to cooperative clusters using the Pareto optimality particle swarm optimization algorithm. In addition, the Nash equilibrium particle swarm optimization algorithm is used for allocating power in the noncooperative clusters. The process of allocating power to cooperative and noncooperative clusters reduces the overall transmission power of the radars. In the experimental section, the proposed method obtained the power consumption of 0.014 to 0.0119 at K = 2, M = 3 and K = 2, M = 3, which is better compared to the existing methodologies—generalized Nash game and cooperative and noncooperative game theory.

中文翻译：

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针对多静态雷达网络的PSO优化的Pareto和Nash均衡博弈功率分配技术

当前，在高速无线通信系统中，多输入多输出雷达可提供更高精度的精确目标检测和更好的目标参数估计。由于空间速度域中目标的稀疏性，这项研究主要集中在功率分配上，以提高雷达的性能。首先，使用核模糊C均值算法对雷达进行聚类。接下来，根据使用核模糊C均值算法测得的距离提取合作和非合作集群。使用帕累托最优粒子群优化算法将功率分配给合作集群。此外，纳什均衡粒子群优化算法用于非合作集群中的功率分配。将功率分配给合作和非合作集群的过程会降低雷达的整体传输功率。在实验部分，所提出的方法获得的功耗为0.014至0.0119。K  = 2，M  = 3，K  = 2，M  = 3，这比现有方法（广义纳什博弈以及合作与非合作博弈论）更好。