Sparse Reconstruction-based Direction of Arrival Estimation for MIMO Radar in the Presence of Unknown Mutual Coupling

To improve the accuracy of Direction Of Arrival (DOA) estimation in Multiple Input Multiple Output (MIMO) radar systems under unknown mutual coupling, we propose a mutual coupling calibration and DOA estimation algorithm based on Sparse Learning via Iterative Minimization (SLIM). The proposed algorithm utilizes the spatial sparsity of target signals and estimates the spatial pseudo-spectra and the mutual coupling matrices of MIMO arrays through cyclic optimization. Moreover, it is hyperparameter-free and guarantees convergence. Numerical examples demonstrate that for MIMO radar systems under unknown mutual coupling conditions, the proposed algorithm can accurately estimate the DOA of targets with small angle separations and relatively high Signal-to-Noise Ratios (SNRs), even with a limited number of samples. In addition, low DOA estimation errors are achieved for targets with large angle separations and small sample sizes, even under low-SNR conditions.