A Sparse Bayesian Learning-Based Main-Beam Deceptive Jamming Suppression Method Using FDA-MIMO Radar

The main-beam deceptive jammings degrade the radar performance greatly due to the difficulty in distinguishing the jammings from the desired signal. To address this problem, a sparse Bayesian learning-based main-beam deceptive jamming suppression method using the frequency diverse array (FDA)-multiple-input multiple-output (MIMO) is proposed. In addition to the angular dimension, an introduced range dimension in FDA-MIMO is utilized to detect the main-beam interferences. Since the range and angle are coupled, a range-angle separation method is given first, where a sparse reconstruction model taking the off-grid problem into consideration is established. Then, a sparse Bayesian learning-based method is proposed to estimate the directions of arrival (DOAs) and the range of sources separately. Considering that the overestimated interference power has little influence on the beamforming performance, the maximum eigenvalue of the sample covariance matrix is adopted as the power of the interferences. Finally, based on the estimated DOAs, ranges, and powers, the interference-plus-noise covariance matrix (IPNCM) is reconstructed to realize the proposed beamformer with the desired signal steering vector. Simulation results demonstrate that the proposed beamformer outperforms the existing methods and can suppress the main-beam interferences significantly.