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

被引:5
作者
Li, Yuqing [1 ]
Yang, Xiaopeng [1 ]
Liu, Feifeng [1 ]
机构
[1] Beijing Inst Technol, Sch Informat & Elect, Beijing Key Lab Embedded Real Time Informat Proc, Beijing 100081, Peoples R China
来源
JOURNAL OF ENGINEERING-JOE | 2019年 / 2019卷 / 19期
基金
中国国家自然科学基金;
关键词
covariance matrices; array signal processing; matrix inversion; radial basis function networks; matrix inversion operation; computational complexity; real-time processing ability; computational cost; fast adaptive beamforming method; robust adaptive beamforming method; complex-valued radial basis function neural network; CRBF neural network; direct matrix inversion; nonlinear mapping processing; array covariance matrix; adaptive weight vector; simulation results; sample matrix inversion algorithm method; RBF neural network beamformer; complex-valued RBF neural network; key techniques;
D O I
10.1049/joe.2019.0275
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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.
引用
收藏
页码:5917 / 5921
页数:5
相关论文
共 10 条
[1]   ADAPTIVE ARRAYS IN AIRBORNE MTI RADAR [J].
BRENNAN, LE ;
MALLETT, JD ;
REED, IS .
IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 1976, 24 (05) :607-615
[2]  
Dubey A.D., 2015, PROC IEEE INT C TREN
[3]  
Monzingo R. A., 2011, INTRO ADAPTIVE ARRAY
[4]   RAPID CONVERGENCE RATE IN ADAPTIVE ARRAYS [J].
REED, IS ;
MALLETT, JD ;
BRENNAN, LE .
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 1974, AE10 (06) :853-863
[5]   A Neural-Network-Based Beamformer for Phased Array Weather Radar [J].
Sallam, Tarek ;
Abdel-Rahman, Adel B. ;
Alghoniemy, Masoud ;
Kawasaki, Zen ;
Ushio, Tomoo .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2016, 54 (09) :5095-5104
[6]  
Savitha R., 2009, PROC TWNCON 2009 IEE
[7]  
Shang F., 2011, J CHINA U POSTS TELE, V18, P158
[8]  
Teitelbaum K., 1991, IEEE Aerospace and Electronics Systems Magazine, V6, P18, DOI 10.1109/62.79673
[9]   High-Dimensional MVDR Beamforming: Optimized Solutions Based on Spiked Random Matrix Models [J].
Yang, Liusha ;
McKay, Matthew R. ;
Couillet, Romain .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2018, 66 (07) :1933-1947
[10]  
Yang X.P., 2015, SCI CHINA INFORM SCI, V58, P1