Tensor-Based Angle-Range Estimation in Near-Field ISAC Systems

With the deployment of extremely large-scale multiple-input multiple-output (MIMO) arrays, integrated sensing and communication (ISAC) systems may operate in the near-field region. In this case, the array manifold matrix depends jointly on angle and range parameters of targets. This paper proposes a near-field angle-range estimation method via low-rank tensor decomposition. The observed data in ISAC systems is modeled as a third-order tensor, which can be decomposed into target parameter-determined factor matrices. To enhance the decomposition accuracy, we introduce a novel constraint on the near-field array manifold in tensor decomposition. The optimization problem associated with the near-field manifold constraint is approximately yet efficiently solved using least squares along with a correlation step. Simulation results validate the effectiveness of the proposed tensor-based framework for angle-range estimation in near-field ISAC systems. The proposed method demonstrates superior estimation accuracy compared to its counterpart without the near-field manifold constraint and a conventional super-resolution method.