Active-IRS-Enabled Target Detection

• Published in: IEEE wireless communications letters p. 1
• Main Authors: Song, Xianxin, Qin, Xiaoqi, Yu, Xianghao, Xu, Jie, Ng, Derrick Wing Kwan
• Format: Journal Article
• Language: English
• Published: IEEE 2025
• Subjects: Active intelligent reflecting surface (IRS); Array signal processing; Covariance matrices; Detectors; joint beamforming optimization; Noise; Object detection; Reflection; Sensors; Symmetric matrices; target detection; Transmission line matrix methods; Vectors
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2025.3579002

This letter studies an active intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) target detection system. In this system, 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 detector and joint beamforming design effectively enhance the detection performance by properly utilizing the active IRS's reflection noise.