access icon openaccess Fast and robust adaptive beamforming method based on complex-valued RBF neural network

This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)
Received 20/02/2019, Accepted 02/05/2019, Published 30/05/2019

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.

Inspec keywords: array signal processing; radial basis function networks; matrix inversion; covariance matrices

Other keywords: key techniques; robust adaptive beamforming method; direct matrix inversion; complex-valued RBF neural network; adaptive weight vector; complex-valued radial basis function neural network; array signal processing; RBF neural network beamformer; array covariance matrix; computational complexity; computational cost; nonlinear mapping processing; sample matrix inversion algorithm method; CRBF neural network; simulation results; matrix inversion operation; fast adaptive beamforming method; real-time processing ability

Subjects: Neural computing techniques; Signal processing and detection; Other topics in statistics

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