Cooperative Game Theoretic Power Allocation Method to Distributed MIMO Radar Sensor Network for Multitarget Detection in Low-Altitude

To enhance air defense efficiency in a distributed multiple input multiple output (D-MIMO) radar sensor network (RSN) for detecting low-altitude targets, this article introduces a cooperative game theoretic power allocation (CGTPA) strategy within power budget constraints. The CGTPA mechanism involves precise power management to effectively mitigate variations and fading in the received signal caused by multipath effects. Thereby, it can capitalize on the abundant scattering characteristics within a multipath environment to achieve enhanced target detectability. The article introduces the concept of multipath distance difference (MDD) by incorporating errors from space-based external information, and subsequently derives the Neyman–Pearson based detection probability and signal-to-interference-plus-noise ratio (SINR). Further, SINR is utilized as the metric for RSN target detection performance, and an optimization model is established based on the max–min criterion. Considering the non-convex characteristic of the optimization model, cooperative game theory provides a vivid and flexible framework for collaborative detection among intelligent antennas in the RSN. The existence and uniqueness of the core are analyzed and reasoned. Furthermore, the Shapley values, derived based on the fairness principle, are utilized to address the cooperative game power allocation problem. Weighted graph techniques are employed to simplify computational complexity, resulting in a power allocation scheme that adheres to Pareto optimality and fairness principles. Numerical results validate our theoretical analysis and the effective performance of the proposed algorithm, which approaches optimal RA method with significant instantaneity.