Near-Field Sensing in Extremely Large-Scale MIMO Systems: A Multi-Layer Reconstruction Mechanism Based Compressive Sensing Approach

The emergence of extremely large-scale MIMO (XLMIMO) has made target sensing in near-field environments crucial. However, the vast number of antennas and the big size of near-field dictionary matrix (DM) result in substantial pilot overhead for beam training algorithms and significant computational complexity for subspace algorithms. Moreover, there is few of work capable of accurately obtaining information beyond target location, such as radar cross-section (RCS). To this end, we propose a high-precision, low-pilot-overhead off-grid compressive sensing (CS) algorithm capable of jointly estimating target's location and RCS-the Multi-Layer Reconstruction Linear Approximation Variational Bayesian Inference (MLR-LA-VBI) algorithm. Specifically, the entire algorithm is divided into two phases. In the first phase, we propose the Multi-Layer Reconstruction (MLR) mechanism to reconstruct a surrogate DM. In the second phase, based on the surrogate DM, thus proposing the MLR-LA-VBI algorithm for joint estimation of target location and RCS. The final simulation results verify the superior performance of the proposed algorithm.