Detection and Multiparameter Estimation for NLoS Targets: An RIS-Assisted Framework

Abstract

Reconfigurable intelligent surface (RIS) has the potential to enhance sensing performance, due to its capability of reshaping the echo signals. Different from the existing literature, which has commonly focused on RIS beamforming optimization, in this paper, we pay special attention to designing effective signal processing approaches to extract sensing information from RIS-reshaped echo signals. To this end, we investigate an RIS-assisted non-line-of-sight (NLoS) target detection and multi-parameter estimation problem in orthogonal frequency division multiplexing (OFDM) systems. To address this problem, we first propose a novel detection and direction estimation framework, including a low-overhead hierarchical codebook that allows the RIS to generate three-dimensional beams with adjustable beam direction and width, a delay spectrum peak-based beam training scheme for detection and direction estimation, and a beam refinement scheme for further enhancing the accuracy of the direction estimation. Then, we propose a target range and velocity estimation scheme by extracting the delay-Doppler information from the RIS-reshaped echo signals. Numerical results demonstrate that the proposed schemes can achieve a $99.7\%$ target detection rate, a $10^{-3}$-rad level direction estimation accuracy, and a $10^{-6}$-m/$10^{-5}$-m/s level range/velocity estimation accuracy.