Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
A new adaptive algorithm for minor component analysis
Signal Processing
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
Neural methods for antenna array signal processing: a review
Signal Processing
On Convergence Conditions of an Extended Projection Neural Network
Neural Computation
A recursive least squares implementation for LCMP beamforming underquadratic constraint
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Calculating an optimal beamforming weight is a main task of beamforming. Quadratic constraints on the weight vector of an adaptive linearly constrained minimum power (LCMP) beamformer can improve robustness to pointing errors and to random perturbations in sensor parameters. This paper presents a neural network approach to the robust LCMP beamformer with the quadratic constraint. Compared with the existing neural networks for the LCMP beamformer, the proposed neural network converges fast to an optimal weight. Compared with the existing adaptive algorithms for the robust LCMP beamformer, in addition to parallel implementation, the proposed neural network is guaranteed to converge exponentially to an optimal weight. Simulations demonstrate that the proposed neural network has better interference suppression and faster convergence than the existing neural networks and the adaptive algorithms.