TY - CHAP
T1 - Large Array Beamforming
AU - Benesty, Jacob
AU - Huang, Gongping
AU - Chen, Jingdong
AU - Pan, Ningning
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - In this chapter, we study beamforming with very large arrays, i.e., arrays that contain a very large number of microphones. Conventional beamforming in this context, where a simple complex frequency-dependent weight is applied to each microphone, may not be very practical for obvious reasons such as high complexity, difficulty to accurately estimate statistics of the signals, and dealing with large matrices that can be very ill conditioned. Here, instead, we tackle this problem from a low-rank beamforming perspective, whose flexibility can lead to better performance.
AB - In this chapter, we study beamforming with very large arrays, i.e., arrays that contain a very large number of microphones. Conventional beamforming in this context, where a simple complex frequency-dependent weight is applied to each microphone, may not be very practical for obvious reasons such as high complexity, difficulty to accurately estimate statistics of the signals, and dealing with large matrices that can be very ill conditioned. Here, instead, we tackle this problem from a low-rank beamforming perspective, whose flexibility can lead to better performance.
KW - Kronecker product decomposition
KW - Large array beamforming
KW - Optimal beamformer
UR - http://www.scopus.com/inward/record.url?scp=85168684090&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-36974-2_10
DO - 10.1007/978-3-031-36974-2_10
M3 - 章节
AN - SCOPUS:85168684090
T3 - Springer Topics in Signal Processing
SP - 205
EP - 223
BT - Springer Topics in Signal Processing
PB - Springer Science and Business Media B.V.
ER -