Leveraging Rough-Relay-Surface Scattering for Non-Line-of-Sight mmWave Radar Sensing

Non-line-of-sight (NLOS) sensing is essential for unmanned robots and intelligent transportation systems, as it enables the sensor to detect targets around street corners, reducing the collision risk. Existing NLOS millimeter-wave (mmWave) radar technologies are based on third-order bounce geometry and utilize a full specular reflection path on smooth relay surfaces to detect targets. However, these works primarily concentrate on ideal lab environments, which pose challenges in wild street scenarios with intricately rough-relay-surface (RRS), such as stone walls and rocks, where nonflat planar surfaces usually exist near a corner. In this article, we present an NLOS sensing system that employs a single commodity mmWave radar to recover a hidden target from multiple scattering paths caused by RRS. The core contribution of the NLOS system is a high-resolution hidden target recovery algorithm by leveraging the multiple scattering paths. Specifically, leveraging knowledge from stochastic geometry and electromagnetic roughness, a microfacets model is used to characterize the random RRS. To deduce the tensor signal model of NLOS mmWave radar sensing with multiinput–multioutput (MIMO) antennas, we first profile the nonlinear geometry relationship among the RRS scattering paths, then focus on the path reflected from each scattering point. Built upon the model, we design a novel stochastic geometry-aided three-stage recovery (SGTR) algorithm for NLOS sensing, which allows the use of estimated virtual ghost targets rather than considering them as a disturbance. We evaluate the effectiveness of the proposed NLOS sensing technique via both simulations and experimental tests.