Subspace superdirective beamformers based on joint diagonalization
Changlei Li, Jacob Benesty, Gongping Huang, Jingdong Chen · 2016
Although they have been intensively studied and used in many applications due to their high directivity factor (DF), superdirective beamformers are sensitive to sensor noise and mismatch between sensors. This paper studies the problem of superdirective beamforming combined with the joint diagonalization method. We develop a subspace superdirective beamforming approach, which can achieve a good compromise between a high DF and white noise amplification. Simulations are performed to justify our theoretical analysis and demonstrate the good properties of this subspace superdirective beamforming approach.