Robust Tensor Decomposition Approach for DOA Estimation With EMVS-MIMO Radar

Parameter estimation in electromagnetic vector sensor (EMVS) multiple-input multiple-output (MIMO) radar faces challenges due to spatial noise and small array apertures. Therefore, a robust tensor decomposition approach, which is tailored for a monostatic EMVS-MIMO radar with sparse L-shaped, is proposed for high-resolution 2-D direction-of-arrival (DOA) estimation by constructing a covariance tensor to suppress spatial noise and utilizing higher order singular value decomposition (HOSVD) to obtain an exact subspace. Subsequently, the vector-cross product (VCP) technique achievable by the EMVS is utilized to obtain low-resolution but unique DOA estimates, and the estimation of signal parameters with rotational invariance technique (ESPRIT) technique based on sparse uniform array geometry is exploited to obtain high-resolution but ambiguous DOA estimates. By combining the characteristics of both, a unique high-resolution DOA estimate is derived. The results indicate that the framework exhibits better estimation accuracy under low signal-to-noise ratios compared with the existing methods. Furthermore, it is more adaptable than current sparse array methods. Experimental simulation results validate the correctness of the theoretical derivations.