Contemporary Approaches to Radar Detection: Robust Statistics, Reinforcement Learning, and Reconfigurable Intelligent Surfaces

Abstract

Radar detection is one of the most fundamental problems in wireless signal processing and has been thoroughly studied for decades. As such, there is very well established theory for radar detection under a myriad of different system models; however, as the machine that is research continues to churn, new advancements are made every day-- compelling the reliable, classical techniques, which serve as pillars of our understanding of radar signal processing, to adapt or improve. To this end, we explore the use of robust statistics, reinforcement learning (RL), and reconfigurable intelligent surfaces (RIS) to improve upon existing radar detection techniques. This work focuses only on monostatic co-located MIMO radar and radar networks. First, we look at the role of robust detection statistics and RL in radar detection. Because RL is a type of online learning, RL based radar detection is well suited for dynamic environments; however, typically, constant false alarm rate (CFAR) radar detection either requires a priori knowledge of noise and clutter distributions or the collection of secondary data. We solve these issues by leveraging detection techniques developed for the massive MIMO (MMIMO) regime for CFAR detection in unknown noise and clutter distributions to perform CFAR detection in unknown noise and clutter distributions with fewer antennas. We provide necessary conditions under which our detection technique (statistic) is valid; furthermore, we pair this detection technique with RL to learn where targets are likely to appear in a predefined, fixed search area, to efficiently perform multi-target detection with MIMO radar. Secondly, we generalize such RL based multi-target detection techniques from a single co-located MIMO radar to a network of co-located MIMO radars which collaborate to efficiently perform detection in a given search area. We develop both centralized and decentralized signal fusion techniques for CFAR detection with radar networks in unknown noise and clutter distributions. We discuss the generalization of our RL algorithm for a single radar to a network of cognitive radars and provide a simulation study to analyze performance of centralized and decentralized detection. Finally, RIS is a new and exciting area of research in wireless communications and sensing. We leverage work showing improved performance in classical RIS-aided radar systems by introducing beyond diagonal RIS-aided radar. We develop beyond diagonal RIS (BD-RIS) configuration optimization specifically for a radar setting by leveraging prior art in BD-RIS optimization. Through simulation, we show improvements in performance over classical RIS-aided radar.