A Game-Theoretic Approach for High-Resolution Automotive FMCW Radar Interference Avoidance

Nonlinear frequency hopping has emerged as a promising approach for mitigating interference and enhancing range resolution in automotive FMCW radar systems. Achieving an optimal balance between high range-resolution and effective interference mitigation remains challenging, especially without centralized frequency scheduling. This paper presents a game-theoretic framework for interference avoidance, in which each radar operates as an independent player, optimizing its performance through decentralized decision-making. We examine two equilibrium concepts--Nash Equilibrium (NE) and Coarse Correlated Equilibrium (CCE)--as strategies for frequency band allocation, with CCE demonstrating particular effectiveness through regret minimization algorithms. We propose two interference avoidance algorithms: Nash Hopping, a model-based approach, and No-Regret Hopping, a model-free adaptive method. Simulation results indicate that both methods effectively reduce interference and enhance the signal-to-interference-plus-noise ratio (SINR). Notably, No-regret Hopping further optimizes frequency spectrum utilization, achieving improved range resolution compared to Nash Hopping.

Enhanced Automotive Radar Collaborative Sensing By Exploiting Constructive Interference

Automotive radar emerges as a crucial sensor for autonomous vehicle perception. As more cars are equipped radars, radar interference is an unavoidable challenge. Unlike conventional approaches such as interference mitigation and interference-avoiding technologies, this paper introduces an innovative collaborative sensing scheme with multiple automotive radars that exploits constructive interference. Through collaborative sensing, our method optimally aligns cross-path interference signals from other radars with another radar's self-echo signals, thereby significantly augmenting its target detection capabilities. This approach alleviates the need for extensive raw data sharing between collaborating radars. Instead, only an optimized weighting matrix needs to be exchanged between the radars. This approach considerably decreases the data bandwidth requirements for the wireless channel, making it a more feasible and practical solution for automotive radar collaboration. Numerical results demonstrate the effectiveness of the constructive interference approach for enhanced object detection capability.