Open AccessFast and robust adaptive beamforming method based on complex‐valued RBF neural network
Author(s) -
Li Yuqing,
Yang Xiaopeng,
Liu Feifeng
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0275
Subject(s) - adaptive beamformer , sample matrix inversion , artificial neural network , computer science , covariance matrix , beamforming , radial basis function , algorithm , inversion (geology) , computational complexity theory , weight , nonlinear system , signal processing , control theory (sociology) , digital signal processing , artificial intelligence , mathematics , telecommunications , paleontology , physics , control (management) , structural basin , quantum mechanics , lie algebra , pure mathematics , biology , computer hardware
The adaptive beamforming is one of the key techniques for array signal processing. However, the matrix inversion operation in the existing methods will cost a large amount of computational complexity, which results in poor real‐time processing ability. In order to reduce the amount of computational cost, a fast and robust adaptive beamforming method based on complex‐valued radial basis function (CRBF) neural network is proposed. In the proposed method, the CRBF neural network is established, thus the direct matrix inversion is avoided by the nonlinear mapping processing from the array covariance matrix to the adaptive weight vector, and therefore the calculation speed of adaptive weight vectors is increased. Based on the simulation results, the proposed method is verified that the speed of adaptive beamforming is increased compared with sample matrix inversion (SMI) algorithm method and an improved performance is achieved compared with that of conventional real‐valued RBF neural network beamformer.