Study on detection method of power frequency magnetic field disturbance signal for underwater target

A hybrid neural network and attention mechanism (Att-CNN-GRU) is presented to solve the problems of fast attenuation,strong interference,ambiguous disturbance characteristics and ineffective signal detection in magnetic field proximity detection of underwater targets.A method for detecting time seri...

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Autores principales:	Bin TIAN, Shiqiang WEN, Tong HU, Bing LIANG, Hanyu HONG
Formato:	article
Lenguaje:	ZH
Publicado:	Hebei University of Science and Technology 2021
Materias:	signal detection; magnetic field time series; gru neural network; precursory anomaly; power frequency magnetic field detection Technology T
Acceso en línea:	https://doaj.org/article/20074d6988ea45f5a757ae9fae4c857e
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spelling	oai:doaj.org-article:20074d6988ea45f5a757ae9fae4c857e2021-11-23T07:09:07ZStudy on detection method of power frequency magnetic field disturbance signal for underwater target1008-154210.7535/hbkd.2021yx05007https://doaj.org/article/20074d6988ea45f5a757ae9fae4c857e2021-10-01T00:00:00Zhttp://xuebao.hebust.edu.cn/hbkjdx/ch/reader/create_pdf.aspx?file_no=b202105007&flag=1&journal_https://doaj.org/toc/1008-1542A hybrid neural network and attention mechanism (Att-CNN-GRU) is presented to solve the problems of fast attenuation,strong interference,ambiguous disturbance characteristics and ineffective signal detection in magnetic field proximity detection of underwater targets.A method for detecting time series disturbance signal of underwater target with power frequency magnetic field is presented.The method combines CNN,GRU neural network and Attention mechanism to fit the signal,and constructs a classification neural network to classify and identify the target signal.The method is compared with the prediction and detection performance of CNN-LSTM model without attention mechanism and single CNN and LSTM network model.The results show that the error of signal fitting is reduced by [BF]36.24%[BFQ],[BF]14.44%[BFQ],[BF]4.878%[BFQ] and the target detection accuracy is [BF]83.3%[BFQ] compared with the traditional methods.Therefore,the CNN-GRU model with Attention mechanism has better performance than CNN,LSTM and CNN-GRU models.As an auxiliary means,it can effectively solve the problems of weak disturbance signal,unclear disturbance law and more background noise in power frequency magnetic field detection,to realize the fitting and detection of power frequency magnetic disturbance signal to underwater target.[HQ]Bin TIANShiqiang WENTong HUBing LIANGHanyu HONGHebei University of Science and Technologyarticlesignal detection; magnetic field time series; gru neural network; precursory anomaly; power frequency magnetic field detectionTechnologyTZHJournal of Hebei University of Science and Technology, Vol 42, Iss 5, Pp 491-498 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	signal detection; magnetic field time series; gru neural network; precursory anomaly; power frequency magnetic field detection Technology T
spellingShingle	signal detection; magnetic field time series; gru neural network; precursory anomaly; power frequency magnetic field detection Technology T Bin TIAN Shiqiang WEN Tong HU Bing LIANG Hanyu HONG Study on detection method of power frequency magnetic field disturbance signal for underwater target
description	A hybrid neural network and attention mechanism (Att-CNN-GRU) is presented to solve the problems of fast attenuation,strong interference,ambiguous disturbance characteristics and ineffective signal detection in magnetic field proximity detection of underwater targets.A method for detecting time series disturbance signal of underwater target with power frequency magnetic field is presented.The method combines CNN,GRU neural network and Attention mechanism to fit the signal,and constructs a classification neural network to classify and identify the target signal.The method is compared with the prediction and detection performance of CNN-LSTM model without attention mechanism and single CNN and LSTM network model.The results show that the error of signal fitting is reduced by [BF]36.24%[BFQ],[BF]14.44%[BFQ],[BF]4.878%[BFQ] and the target detection accuracy is [BF]83.3%[BFQ] compared with the traditional methods.Therefore,the CNN-GRU model with Attention mechanism has better performance than CNN,LSTM and CNN-GRU models.As an auxiliary means,it can effectively solve the problems of weak disturbance signal,unclear disturbance law and more background noise in power frequency magnetic field detection,to realize the fitting and detection of power frequency magnetic disturbance signal to underwater target.[HQ]
format	article
author	Bin TIAN Shiqiang WEN Tong HU Bing LIANG Hanyu HONG
author_facet	Bin TIAN Shiqiang WEN Tong HU Bing LIANG Hanyu HONG
author_sort	Bin TIAN
title	Study on detection method of power frequency magnetic field disturbance signal for underwater target
title_short	Study on detection method of power frequency magnetic field disturbance signal for underwater target
title_full	Study on detection method of power frequency magnetic field disturbance signal for underwater target
title_fullStr	Study on detection method of power frequency magnetic field disturbance signal for underwater target
title_full_unstemmed	Study on detection method of power frequency magnetic field disturbance signal for underwater target
title_sort	study on detection method of power frequency magnetic field disturbance signal for underwater target
publisher	Hebei University of Science and Technology
publishDate	2021
url	https://doaj.org/article/20074d6988ea45f5a757ae9fae4c857e
work_keys_str_mv	AT bintian studyondetectionmethodofpowerfrequencymagneticfielddisturbancesignalforunderwatertarget AT shiqiangwen studyondetectionmethodofpowerfrequencymagneticfielddisturbancesignalforunderwatertarget AT tonghu studyondetectionmethodofpowerfrequencymagneticfielddisturbancesignalforunderwatertarget AT bingliang studyondetectionmethodofpowerfrequencymagneticfielddisturbancesignalforunderwatertarget AT hanyuhong studyondetectionmethodofpowerfrequencymagneticfielddisturbancesignalforunderwatertarget
_version_	1718416811912855552

Study on detection method of power frequency magnetic field disturbance signal for underwater target

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