Intelligent Reflecting Surface-Assisted NLOS Sensing via Tensor Decomposition

We consider the problem of intelligent reflecting surface (IRS) assisted target sensing in a non-line-of-sight (NLOS) scenario, where the line-of-sight (LOS) path between the access point (AP) and the target is blocked by obstacles, and an IRS is employed to facilitate the AP to sense the targets that are distributed in its NLOS region. The AP transmits orthogonal frequency division multiplexing (OFDM) pulses and then perceives the targets based on the echoes from the AP-IRS-targets-IRS-AP link. To resolve an inherent scaling ambiguity for IRS-assisted NLOS sensing, we propose a two-phase sensing scheme by exploiting the diversity in the illumination pattern of the IRS across two different phases. Specifically, the received echo signals from the two phases are constructed as two third-order tensors. Then a CANDECOMP/PARAFAC decomposition (CPD)-based method is developed to extract target parameters from the two constructed tensors. Simulation results are provided to show the effectiveness of the proposed method.