Covariance Tensor Decomposition for NLOS Direction Finding in RIS-Aided Bistatic MIMO Radar

This letter investigates the problem of direction-of-departure (DOD) and direction-of-arrival (DOA) estimation for non-line-of-sight (NLOS) targets in bistatic multiple-input multiple-output (MIMO) radar systems assisted by an intelligent reflecting surface (IRS). To tackle this issue, we propose a covariance tensor subspace-based algorithm. First, the received data is modeled within a tensor framework to preserve their inherent multi-dimensional spatiotemporal structure. Then, a fourth-order covariance tensor is constructed by computing correlations along the temporal dimension. Using the higher-order singular value decomposition (HOSVD), the signal subspace matrix is derived from this covariance tensor. The receive steering matrix is accurately reconstructed by exploiting the property of the Khatri–Rao product for full-column-rank matrices. Based on the estimated signal subspace and the reconstructed steering matrix, DOD and DOA estimation is efficiently performed via the rotational invariance technique combined with a one-dimensional correlation-based method, which provides automatic parameter pairing. Simulation results validate the superiority and effectiveness of the proposed algorithm in estimating angles.