A Tensorial Target Detection Framework for MIMO Wireless Sensing System

Multiple-input multiple-output wireless sensing systems achieve high-resolution detection through spatial diversity. However, they suffer from reliability degradation under low signal-to-noise ratio (SNR) conditions. Conventional matrix-based methods may discard critical target information embedded in inter-dimensional signal correlations due to dimension-reduction flattening operations. To overcome these limitations, this paper proposes a tensorial target detection (TTD) framework combining noise reduction and enhanced detection specifically designed for high-dimensional processing. Firstly, we propose a two-stage tensorial noise reduction (TNR) method based on the minimum mean square error criterion and the alternating least square iteration strategy to remove noise in high-order signal space. We further identify the sub-optimal performance caused by inter-dimensional noise coupling at the first stage of TNR, and resolve the issue via rank-constrained optimization for noise-target subspace separation at the second stage of TNR. Then, we develop an augmented tensorial detector based on cross-shaped spatial partitioning (CSP) to enhance detection performance, which jointly optimizes detection thresholds by adaptively refining noise estimation. Finally, field measurements confirm the TTD framework's operational validity, while in simulation the TNR method achieves 5 dB SNR improvement over 2D method, and the CSP-based detector delivers a 21% enhancement in detection probability over conventional approaches. © 2026 IEEE