Sparsity-based robust adaptive beamforming exploiting coprime array
Kailun Liu, Yimin Daniel Zhang · 2017
In this paper, a novel sparsity-based adaptive beamforming algorithm is proposed to achieve effective interference cancellation using coprime arrays. To reconstruct the interference-plus-noise covariance matrix and obtain the steering vector of the desired signal required for robust beamforming, the power and directions-of-arrival (DOAs) of signals are estimated in the context of compress sensing. The results are then refined to obtain a more accurate estimation of the signal power so as to ensure effective interference cancellation. The power and DOA estimation is performed using the virtual array aperture of a coprime array in order to achieve improved estimation accuracy as compared to the results based directly on the physical array. The estimated power and DOA information are then used to reconstruct the interference-plus-noise covariance matrix and implement a robust adaptive beam-former. Simulation results demonstrate the effectiveness of the proposed algorithm.