Hyperparameter free sparse estimation for MIMO radar direction finding in the presence of unknown gain-phase errors

To improve the accuracy of direction of arrival (DOA) estimation in multiple-input-multiple-output (MIMO) radar systems under unknown gain-phase errors, we propose a gain-phase error calibration and DOA estimation algorithm based on sparse learning via iterative minimization (SLIM). The proposed algorithm harnesses the spatial sparsity of the target signals, and estimates the spatial spectrum and gain-phase error matrices of MIMO arrays through a cyclic optimization procedure. Numerical results show that even in the presence of unknown gain-phase uncertainties, the proposed algorithm can accurately estimate the target DOA using a limited number of samples.