DOD and DOA estimation for MIMO radar based on combined MUSIC and sparse Bayesian learning

The direction of departure (DOD) and direction of arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar is discussed, and a method combining the multiple signal classification (MUSIC) and sparse Bayesian learning (SBL) is proposed. The proposed method firstly finds the relationship between DOD and DOA via reduced-dimension (RD) MUSIC, and then the dictionary can be constructed with only one-dimensional angle. Finally, DOD and DOA are estimated via SBL. The proposed method estimates two-dimensional parameter without the loss of degrees of freedom (DOF) and uses fully the signal and noise subspaces. Simulation results show that it works better than RD-MUSIC, especially under low signal to noise ratio (SNR).