Maximum Likelihood Distortionless Response Beamformer Based on the Constrained Kalman Filter

‍ ‍Adaptive beamforming is an effective technique for high-quality sound acquisition. The recently proposed maximum likelihood distortionless response （MLDR） beamformer is promising because it does not require an explicit noise covariance matrix as input. In this paper， based on the constrained Kalman filter， an MLDR beamformer is proposed and its low-complexity implementation is also presented. The measurement equation is constructed using the cost function of the MLDR， and the beamformer weights are described by a first-order Markov process. Additionally， a diagonal form of the constrained Kalman filter is presented to further improve the computational efficiency. Experimental results on CHiME-3 indicate that the proposed beamformer has a similar performance to the existing online MLDR beamformer， but the former is computationally more efficient.