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Multi-source underwater DOA estimation using PSO-BP neural network based on high-order cumulant optimization

文献类型：期刊论文

作者Chen, Haihua1; Zhang, Jingyao1; Jiang, Bin2; Cui, Xuerong3; Zhou, Rongrong3; Zhang, Yucheng1
刊名CHINA COMMUNICATIONS
出版日期2023-05-10
页码18
ISSN号1673-5447
关键词Direction-of-arrival estimation Estimation Neural networks Mathematical models Training Covariance matrices Biological neural networks particle swarm optimization (PSO) algorithm PSO-BP neural network gaussian colored noise multiple sources higher-order cumulant
DOI10.23919/JCC.ea.2021-0031.202302
英文摘要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.
资助项目Strategic Priority Research Program of Chinese Academy of Sciences[XDA28040000] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA28120000] ; Natural Science Foundation of Shandong Province[ZR2021MF094] ; Key R & D Plan of Shandong Province[2020CXGC010804] ; Central Leading Local Science and Technology Development Special Fund Project[YDZX2021122] ; Science & Technology Specific Projects in Agricultural High-tech Industrial Demonstration Area of the Yellow River Delta[2022SZX11]
WOS研究方向Telecommunications
语种英语
出版者CHINA INST COMMUNICATIONS
WOS记录号WOS:000988374000001
源URL[http://119.78.100.204/handle/2XEOYT63/21447]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhang, Jingyao
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing 100080, Peoples R China
2.China YITUO Grp Co Ltd, Luoyang 471000, Peoples R China
3.China Univ Petr East China, Coll Oceanog & Space Informat, Qingdao 266580, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Haihua,Zhang, Jingyao,Jiang, Bin,et al. Multi-source underwater DOA estimation using PSO-BP neural network based on high-order cumulant optimization[J]. CHINA COMMUNICATIONS,2023:18.
APA Chen, Haihua,Zhang, Jingyao,Jiang, Bin,Cui, Xuerong,Zhou, Rongrong,&Zhang, Yucheng.(2023).Multi-source underwater DOA estimation using PSO-BP neural network based on high-order cumulant optimization.CHINA COMMUNICATIONS,18.
MLA Chen, Haihua,et al."Multi-source underwater DOA estimation using PSO-BP neural network based on high-order cumulant optimization".CHINA COMMUNICATIONS (2023):18.

入库方式： OAI收割

来源：计算技术研究所

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