title: A recurrent neural network for adaptive beamforming and array correction.
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.affiliation: School of Electronics and Information Engineering, Southwest University, Chongqing 400715, PR China. Electronic address: chj11711@163.com.
authors.forename: Hangjun
authors.initials: H
authors.lastname: Che
authors.affiliation: School of Electronics and Information Engineering, Southwest University, Chongqing 400715, PR China. Electronic address: licdswu@163.com.
authors.forename: Chuandong
authors.initials: C
authors.lastname: Li
authors.affiliation: School of Electronics and Information Engineering, Southwest University, Chongqing 400715, PR China. Electronic address: hexingdoc@swu.edu.cn.
authors.forename: Xing
authors.initials: X
authors.lastname: He
authors.affiliation: Department of Mathematics, Texas A&M University at Qatar, Doha, P.O.Box 23874, Qatar. Electronic address: tingwen.huang@qatar.tamu.edu.
authors.forename: Tingwen
authors.initials: T
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer; D011340:Problem Solving
keywords: Array state; Beamforming optimization; Exact solutions; Recurrent neural network