Detection and Multi-Parameter Estimation for NLOS Targets: An IRS-assisted Framework

TL;DR

This paper introduces an IRS-assisted framework for NLOS target detection and multi-parameter estimation, focusing on signal processing techniques to extract sensing information from IRS-reshaped echo signals in OFDM systems.

Contribution

It proposes a novel detection and estimation framework with hierarchical codebooks, beam training, and refinement schemes tailored for IRS-assisted NLOS sensing.

Findings

Achieves 99.7% target detection rate

Direction estimation accuracy of 10^{-3} radians

Range and velocity estimation accuracy of 10^{-6} m and 10^{-5} m/s

Abstract

Intelligent reflecting surface (IRS) 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 IRS beamforming optimization, in this paper, we pay special attention to designing effective signal processing approaches to extract sensing information from IRS-reshaped echo signals. To this end, we investigate an IRS-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 IRS 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 IRS-reshaped echo signals. Numerical results demonstrate that the proposed schemes can achieve 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.