Active-IRS-Enabled Target Detection

TL;DR

This paper proposes an active IRS-enabled system for non-line-of-sight target detection, designing optimal detectors and joint beamforming to significantly improve detection probability over passive IRS systems.

Contribution

It introduces a novel active IRS-based detection framework with optimized beamforming, exploiting active IRS elements and echo signals for enhanced NLoS target detection.

Findings

Joint beamforming significantly improves detection probability.

Active IRS outperforms passive IRS in detection performance.

Optimal detector design effectively utilizes both transmit signals and reflection noise.

Abstract

This letter studies an active intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) target detection system, in which an active IRS equipped with active reflecting elements and sensors is strategically deployed to facilitate target detection in the NLoS region of the base station (BS) by processing echo signals through the BS-IRS-target-IRS link. First, we design an optimal detector based on the Neyman-Pearson (NP) theorem and derive the corresponding detection probability. Intriguingly, it is demonstrated that the optimal detector can exploit both the BS's transmit signal and the active IRS's reflection noise for more effective detection. Subsequently, we jointly optimize the transmit beamforming at the BS and the reflective beamforming at the active IRS to maximize the detection probability, subject to the maximum transmit power constraint at the BS, as well as the maximum amplification power and gain constraints at the active IRS. Finally, simulation results unveil that the proposed joint beamforming design significantly enhances the detection probability, with the active IRS outperforming its fully- and semi-passive counterparts in detection performance.