Tensor Reconstruction-Based Sparse Array 2-D DOA Estimation of Mixed Coherent and Uncorrelated Signals

This paper addresses the direction-of-arrival (DOA) estimation problem of mixed coherent and uncorrelated signals using a sparse rectangular array, where tensor reconstruction is employed to preserve the structure of multi-dimensional array signals. In the proposed approach, we first estimate the DOAs of uncorrelated signals using the subspace algorithm. After eliminating the contribution of uncorrelated signals from the covariance tensor, a structural tensor decorrelation process is introduced to decorrelate the resulting coherent covariance tensor. The canonical polyadic decomposition method is employed to the decorrelated covariance tensor to detect the coherent signals. The conditions of signal resolvability are analyzed.