This paper investigates a countermeasure model between a multistatic multiple-input multiple-output (MIMO) radar network and multiple targets in the presence of continuous surface clutter. As a member of the multistatic MIMO radar network, the main purpose of each radar is to minimize its transmit power under a certain target detection criterion. Based on the selfishness of each radar, a strategic non-cooperative game (SNG) framework between MIMO radars is constructed. For the SNG between MIMO radars, the existence and uniqueness of the Nash equilibrium (NE) solution are proved strictly. And then, an iterative power allocation strategy for each MIMO radar is developed using optimization theory. The receive beamformer weight vectors of the multistatic MIMO radars are obtained by minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV) to suppress cross-channel interferences, respectively. Furthermore, two joint beamforming and power allocation game algorithms are proposed, which converge to the NE of the game. Finally, in order to illustrate the superiority of the proposed algorithms, we compare them with the relevant game algorithm. Numerical results are provided to show the advantages of the proposed algorithms in terms of power allocation and interference suppression.

本文研究了在存在连续表面杂波的情况下，多静态多输入多输出（MIMO）雷达网络与多个目标之间的对策模型。作为多静态MIMO雷达网络的成员，每个雷达的主要目的是在一定的目标检测标准下将其发射功率降至最低。基于每个雷达的自私性，构建了MIMO雷达之间的战略性非合作游戏（SNG）框架。对于MIMO雷达之间的SNG，严格证明了纳什均衡（NE）解的存在性和唯一性。然后，使用优化理论为每个MIMO雷达开发了一种迭代功率分配策略。分别通过最小方差无失真响应（MVDR）和线性约束最小方差（LCMV）来获得多静态MIMO雷达的接收波束成形器权重矢量，以分别抑制跨信道干扰。此外，提出了两种联合波束成形和功率分配游戏算法，它们收敛到游戏的NE。最后，为了说明所提出算法的优越性，我们将它们与相关的博弈算法进行了比较。数值结果表明了该算法在功率分配和干扰抑制方面的优势。为了说明所提出算法的优越性，我们将它们与相关的博弈算法进行比较。数值结果表明了该算法在功率分配和干扰抑制方面的优势。为了说明所提出算法的优越性，我们将它们与相关的博弈算法进行比较。数值结果表明了该算法在功率分配和干扰抑制方面的优势。