A recurrent neural network for adaptive beamforming and array correction. (August 2016)

• Record Type: Journal Article
• Authors: Che, Hangjun; Li, Chuandong; He, Xing; Huang, Tingwen
• Is Part Of: Neural networks. Volume 80(2016)
• Page Start: 110
• Page End: 117
• Publication Date: 2016-08
• Subjects: Beamforming optimization -- Exact solutions -- Recurrent neural network -- Array state; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2016.04.010
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: In this paper, a recurrent neural network (RNN) is proposed for solving adaptive beamforming problem. In order to minimize sidelobe interference, the problem is described as a convex optimization problem based on linear array model. RNN is designed to optimize system's weight values in the feasible region which is derived from arrays' state and plane wave's information. The new algorithm is proven to be stable and converge to optimal solution in the sense of Lyapunov. So as to verify new algorithm's performance, we apply it to beamforming under array mismatch situation. Comparing with other optimization algorithms, simulations suggest that RNN has strong ability to search for exact solutions under the condition of large scale constraints.