DOD and DOA Estimation From Incomplete Data Based on PARAFAC and Atomic Norm Minimization Method
Sizhe Gao, Hui Ma, Hongwei Liu, Yang Yang
Abstract
In this article, we propose an efficient direction of departure (DOD) and direction of arrival (DOA) estimation method for bistatic multiple-input multiple-output (MIMO) radar with faulty arrays. A third-order tensor model is built, and the measurement 3-D structure can be better utilized than the traditional matrix model. Subsequently, the atomic norm minimization (ANM) technique is used to further improve the angle estimation performance. Furthermore, we found in the research process that when the faulty arrays still maintain the symmetry property, the measurement tensor can be converted to the real-valued domain by the forward–backward averaging technique and the unitary transform technique. The new algorithm we proposed exploits the multidimensional structure of the signal without estimating the signal subspace. Comparing with traditional matrix completion (MC) methods, it has a better performance in terms of robustness and resolving correlated targets. Also, the algorithm proposed in this article does not require angle pairing. Simulation results verify the effectiveness of the proposed algorithm.