Robust Adaptive Wideband Beamforming Based on Time Frequency Distribution
Yaqi Liu, Chengcheng Liu, Dexiu Hu, Yongjun Zhao · IEEE Transactions on Signal Processing · 2019
Many robust adaptive wideband beamforming algorithms have been carried out to preserve the signal of interest, but their performance can be still degraded by the mismatches of steering vector and covariance matrix. Therefore, in this paper, a novel robust adaptive wideband beamformer based on the time-frequency distribution is proposed, which can achieve high output signal-to-interference-plus-noise ratio close to the optimal one even with perturbed array manifold and limited snapshots. It transforms the received signal into the time-frequency domain, and gives a method to select the single-source points of wideband signals. With the single-source points, it reconstructs the interference-plus-noise covariance matrix and estimates the steering vector without using the imprecise prior information about the array manifold, guaranteeing high estimation accuracy. Simulation results demonstrate that the proposed method outperforms other adaptive wideband beamformers, and achieves excellent output performance over a broad range of direction and sensor position errors even with small number of snapshots.