Optimizing Low-Grazing Angle Detection for Maneuvering Targets in Cognitive MIMO Radar Networks: A Shapley Value Approach

Abstract

In the domain of vehicular engineering, multipath detection plays a critical role in the accurate assessment of the environment. This paper introduces a Shapley value-based power resource allocation (SVPRA) method developed for low-grazing angle detection in distributed multiple-input multiple-output (D-MIMO) radar sensor networks (RSN). The SVPRA leverages multipath echoes for improved detection performance through coordinated power resource allocation (PRA). A systematic multipath scattering model with uncertainty identifies four independent spatial paths, highlighting the impact of multipath effects (ME) fluctuations on detection. The PRA is then formulated as a non-convex Max-min optimization problem, incorporating the signal-to-interference plus noise ratio (SINR) indicator and modeling cognitive collaboration detection as a cooperative game. The application of the PRA rule has been mathematically proven to consistently enhance the detection performance. To address convergence challenges in multi-target scenarios, a fine-tuning method is introduced, providing a provisional iteration outcome, succeeded by a greedy selection process to achieve a feasible solution. Simulation results validate that the SVPRA method effectively enhances detection performance while reducing the system's timeliness load.