Multi-Source Underwater DOA Estimation Using PSO-BP Neural Network Based on High-Order Cumulant Optimization  

作　　者：Haihua Chen Jingyao Zhang Bin Jiang Xuerong Cui Rongrong Zhou Yucheng Zhang 

机构地区：[1]Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100080,China [2]College of Oceanography and Space Informatics,China University of Petroleum(East China),Qingdao 266580,China [3]China YITUO Group Co.,Ltd,Luoyang 471000,China

出　　处：《China Communications》2023年第12期212-229,共18页中国通信（英文版）

基　　金：Strategic Priority Research Program of Chinese Academy of Sciences,Grant No.XDA28040000,XDA28120000;Natural Science Foundation of Shandong Province,Grant No.ZR2021MF094;Key R&D Plan of Shandong Province,Grant No.2020CXGC010804;Central Leading Local Science and Technology Development Special Fund Project,Grant No.YDZX2021122;Science&Technology Specific Projects in Agricultural High-tech Industrial Demonstration Area of the Yellow River Delta,Grant No.2022SZX11。

摘　　要：Due to the complex and changeable environment under water,the performance of traditional DOA estimation algorithms based on mathematical model,such as MUSIC,ESPRIT,etc.,degrades greatly or even some mistakes can be made because of the mismatch between algorithm model and actual environment model.In addition,the neural network has the ability of generalization and mapping,it can consider the noise,transmission channel inconsistency and other factors of the objective environment.Therefore,this paper utilizes Back Propagation(BP)neural network as the basic framework of underwater DOA estimation.Furthermore,in order to improve the performance of DOA estimation of BP neural network,the following three improvements are proposed.(1)Aiming at the problem that the weight and threshold of traditional BP neural network converge slowly and easily fall into the local optimal value in the iterative process,PSO-BP-NN based on optimized particle swarm optimization(PSO)algorithm is proposed.(2)The Higher-order cumulant of the received signal is utilized to establish the training model.(3)A BP neural network training method for arbitrary number of sources is proposed.Finally,the effectiveness of the proposed algorithm is proved by comparing with the state-of-the-art algorithms and MUSIC algorithm.

关 键 词：gaussian colored noise higher-order cumulant multiple sources particle swarm optimization(PSO)algorithm PSO-BP neural network 

分 类 号：TN9[电子电信—信息与通信工程]