A recurrent neural network for adaptive beamforming and array correction. Academic Article

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.

authors

• Huang, Tingwen

published proceedings

• Neural Netw

altmetric score

• 1

author list (cited authors)

• Che, H., Li, C., He, X., & Huang, T.

citation count

• 18

complete list of authors

• Che, Hangjun||Li, Chuandong||He, Xing||Huang, Tingwen

publication date

• August 2016

publisher

• Elsevier  Publisher

published in

• Neural Networks  Journal

keywords

• Algorithms
• Array State
• Beamforming Optimization
• Exact Solutions
• Neural Networks, Computer
• Problem Solving
• Recurrent Neural Network

PubMed Central ID

• 27203554

Digital Object Identifier (DOI)

• 10.1016/j.neunet.2016.04.010

start page

• 110

end page

• 117

volume

• 80

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.neunet.2016.04.010