Underwater Target Noise Recognition and Classification Technology based on Multi-Classes Feature Fusion

As one of the main signal sources of underwater acoustic target recognition, the target noise signal is difficult to characterize the characteristics of the target from clearly comparing with the multi-sensor detection technology, which may lead to lower recognition rate and higher false alarm rate...
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Format:	E-Artikel
Sprache:	Chinesisch

Erschienen:	2020

Schlagwörter:	underwater acoustic target recognition underwater acoustic target noise multi-class feature fusion feature level fusion recognition method decision level fusion recognition method

Übergeordnetes Werk:	In: Xibei Gongye Daxue Xuebao - The Northwestern Polytechnical University, 2020, 38(2020), 2, Seite 366-376
Übergeordnetes Werk:	volume:38 ; year:2020 ; number:2 ; pages:366-376

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1051/jnwpu/20203820366

Katalog-ID:	DOAJ057705372

Internformat


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allfields	10.1051/jnwpu/20203820366 doi (DE-627)DOAJ057705372 (DE-599)DOAJfbfe90c5aa124f5a87e78aaaf385a223 DE-627 ger DE-627 rakwb chi TL1-4050 Underwater Target Noise Recognition and Classification Technology based on Multi-Classes Feature Fusion 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As one of the main signal sources of underwater acoustic target recognition, the target noise signal is difficult to characterize the characteristics of the target from clearly comparing with the multi-sensor detection technology, which may lead to lower recognition rate and higher false alarm rate and seriously restricts the function of underwater acoustic detection system. In order to solve this problem, a multi-layers LSTM underwater acoustic target noise feature extraction model is established by using the long short term memory network. The information features such as time-domain envelope of target noise, Demon line spectrum and Mel frequency cepstrum coefficient are extracted, and a subset of multi-classes features is constructed. On this basis, the feature level fusion recognition and classification model based on the multi-classes features subset and the decision level fusion recognition and classification model based on the D-S evidence theory are established, and the above-mentioned models are tested by using the sample database. The difference of classification result between the multi-classes feature fusion and the single class feature recognition classification is compared, and the above model is tested and verified by using the relevant test data of port basin verification experiment. The correlation results show that the present intelligent recognition and classification method of underwater target noise based on the multi-classes feature fusion is more robust, and the recognition rate and false alarm rate of underwater target are better than those of single category feature discrimination method. underwater acoustic target recognition underwater acoustic target noise multi-class feature fusion feature level fusion recognition method decision level fusion recognition method Motor vehicles. Aeronautics. Astronautics In Xibei Gongye Daxue Xuebao The Northwestern Polytechnical University, 2020 38(2020), 2, Seite 366-376 (DE-627)594775612 (DE-600)2486045-1 26097125 nnns volume:38 year:2020 number:2 pages:366-376 https://doi.org/10.1051/jnwpu/20203820366 kostenfrei https://doaj.org/article/fbfe90c5aa124f5a87e78aaaf385a223 kostenfrei https://www.jnwpu.org/articles/jnwpu/full_html/2020/02/jnwpu2020382p366/jnwpu2020382p366.html kostenfrei https://doaj.org/toc/1000-2758 Journal toc kostenfrei https://doaj.org/toc/2609-7125 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 38 2020 2 366-376
spelling	10.1051/jnwpu/20203820366 doi (DE-627)DOAJ057705372 (DE-599)DOAJfbfe90c5aa124f5a87e78aaaf385a223 DE-627 ger DE-627 rakwb chi TL1-4050 Underwater Target Noise Recognition and Classification Technology based on Multi-Classes Feature Fusion 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As one of the main signal sources of underwater acoustic target recognition, the target noise signal is difficult to characterize the characteristics of the target from clearly comparing with the multi-sensor detection technology, which may lead to lower recognition rate and higher false alarm rate and seriously restricts the function of underwater acoustic detection system. In order to solve this problem, a multi-layers LSTM underwater acoustic target noise feature extraction model is established by using the long short term memory network. The information features such as time-domain envelope of target noise, Demon line spectrum and Mel frequency cepstrum coefficient are extracted, and a subset of multi-classes features is constructed. On this basis, the feature level fusion recognition and classification model based on the multi-classes features subset and the decision level fusion recognition and classification model based on the D-S evidence theory are established, and the above-mentioned models are tested by using the sample database. The difference of classification result between the multi-classes feature fusion and the single class feature recognition classification is compared, and the above model is tested and verified by using the relevant test data of port basin verification experiment. The correlation results show that the present intelligent recognition and classification method of underwater target noise based on the multi-classes feature fusion is more robust, and the recognition rate and false alarm rate of underwater target are better than those of single category feature discrimination method. underwater acoustic target recognition underwater acoustic target noise multi-class feature fusion feature level fusion recognition method decision level fusion recognition method Motor vehicles. Aeronautics. Astronautics In Xibei Gongye Daxue Xuebao The Northwestern Polytechnical University, 2020 38(2020), 2, Seite 366-376 (DE-627)594775612 (DE-600)2486045-1 26097125 nnns volume:38 year:2020 number:2 pages:366-376 https://doi.org/10.1051/jnwpu/20203820366 kostenfrei https://doaj.org/article/fbfe90c5aa124f5a87e78aaaf385a223 kostenfrei https://www.jnwpu.org/articles/jnwpu/full_html/2020/02/jnwpu2020382p366/jnwpu2020382p366.html kostenfrei https://doaj.org/toc/1000-2758 Journal toc kostenfrei https://doaj.org/toc/2609-7125 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 38 2020 2 366-376
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language	Chinese
source	In Xibei Gongye Daxue Xuebao 38(2020), 2, Seite 366-376 volume:38 year:2020 number:2 pages:366-376
sourceStr	In Xibei Gongye Daxue Xuebao 38(2020), 2, Seite 366-376 volume:38 year:2020 number:2 pages:366-376
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	underwater acoustic target recognition underwater acoustic target noise multi-class feature fusion feature level fusion recognition method decision level fusion recognition method Motor vehicles. Aeronautics. Astronautics
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topic_title	TL1-4050 Underwater Target Noise Recognition and Classification Technology based on Multi-Classes Feature Fusion underwater acoustic target recognition underwater acoustic target noise multi-class feature fusion feature level fusion recognition method decision level fusion recognition method
topic	misc TL1-4050 misc underwater acoustic target recognition misc underwater acoustic target noise misc multi-class feature fusion misc feature level fusion recognition method misc decision level fusion recognition method misc Motor vehicles. Aeronautics. Astronautics
spellingShingle	misc TL1-4050 misc underwater acoustic target recognition misc underwater acoustic target noise misc multi-class feature fusion misc feature level fusion recognition method misc decision level fusion recognition method misc Motor vehicles. Aeronautics. Astronautics Underwater Target Noise Recognition and Classification Technology based on Multi-Classes Feature Fusion
topic_unstemmed	misc TL1-4050 misc underwater acoustic target recognition misc underwater acoustic target noise misc multi-class feature fusion misc feature level fusion recognition method misc decision level fusion recognition method misc Motor vehicles. Aeronautics. Astronautics
topic_browse	misc TL1-4050 misc underwater acoustic target recognition misc underwater acoustic target noise misc multi-class feature fusion misc feature level fusion recognition method misc decision level fusion recognition method misc Motor vehicles. Aeronautics. Astronautics
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title	Underwater Target Noise Recognition and Classification Technology based on Multi-Classes Feature Fusion
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title_full	Underwater Target Noise Recognition and Classification Technology based on Multi-Classes Feature Fusion
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title_sort	underwater target noise recognition and classification technology based on multi-classes feature fusion
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title_auth	Underwater Target Noise Recognition and Classification Technology based on Multi-Classes Feature Fusion
abstract	As one of the main signal sources of underwater acoustic target recognition, the target noise signal is difficult to characterize the characteristics of the target from clearly comparing with the multi-sensor detection technology, which may lead to lower recognition rate and higher false alarm rate and seriously restricts the function of underwater acoustic detection system. In order to solve this problem, a multi-layers LSTM underwater acoustic target noise feature extraction model is established by using the long short term memory network. The information features such as time-domain envelope of target noise, Demon line spectrum and Mel frequency cepstrum coefficient are extracted, and a subset of multi-classes features is constructed. On this basis, the feature level fusion recognition and classification model based on the multi-classes features subset and the decision level fusion recognition and classification model based on the D-S evidence theory are established, and the above-mentioned models are tested by using the sample database. The difference of classification result between the multi-classes feature fusion and the single class feature recognition classification is compared, and the above model is tested and verified by using the relevant test data of port basin verification experiment. The correlation results show that the present intelligent recognition and classification method of underwater target noise based on the multi-classes feature fusion is more robust, and the recognition rate and false alarm rate of underwater target are better than those of single category feature discrimination method.
abstractGer	As one of the main signal sources of underwater acoustic target recognition, the target noise signal is difficult to characterize the characteristics of the target from clearly comparing with the multi-sensor detection technology, which may lead to lower recognition rate and higher false alarm rate and seriously restricts the function of underwater acoustic detection system. In order to solve this problem, a multi-layers LSTM underwater acoustic target noise feature extraction model is established by using the long short term memory network. The information features such as time-domain envelope of target noise, Demon line spectrum and Mel frequency cepstrum coefficient are extracted, and a subset of multi-classes features is constructed. On this basis, the feature level fusion recognition and classification model based on the multi-classes features subset and the decision level fusion recognition and classification model based on the D-S evidence theory are established, and the above-mentioned models are tested by using the sample database. The difference of classification result between the multi-classes feature fusion and the single class feature recognition classification is compared, and the above model is tested and verified by using the relevant test data of port basin verification experiment. The correlation results show that the present intelligent recognition and classification method of underwater target noise based on the multi-classes feature fusion is more robust, and the recognition rate and false alarm rate of underwater target are better than those of single category feature discrimination method.
abstract_unstemmed	As one of the main signal sources of underwater acoustic target recognition, the target noise signal is difficult to characterize the characteristics of the target from clearly comparing with the multi-sensor detection technology, which may lead to lower recognition rate and higher false alarm rate and seriously restricts the function of underwater acoustic detection system. In order to solve this problem, a multi-layers LSTM underwater acoustic target noise feature extraction model is established by using the long short term memory network. The information features such as time-domain envelope of target noise, Demon line spectrum and Mel frequency cepstrum coefficient are extracted, and a subset of multi-classes features is constructed. On this basis, the feature level fusion recognition and classification model based on the multi-classes features subset and the decision level fusion recognition and classification model based on the D-S evidence theory are established, and the above-mentioned models are tested by using the sample database. The difference of classification result between the multi-classes feature fusion and the single class feature recognition classification is compared, and the above model is tested and verified by using the relevant test data of port basin verification experiment. The correlation results show that the present intelligent recognition and classification method of underwater target noise based on the multi-classes feature fusion is more robust, and the recognition rate and false alarm rate of underwater target are better than those of single category feature discrimination method.
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title_short	Underwater Target Noise Recognition and Classification Technology based on Multi-Classes Feature Fusion
url	https://doi.org/10.1051/jnwpu/20203820366 https://doaj.org/article/fbfe90c5aa124f5a87e78aaaf385a223 https://www.jnwpu.org/articles/jnwpu/full_html/2020/02/jnwpu2020382p366/jnwpu2020382p366.html https://doaj.org/toc/1000-2758 https://doaj.org/toc/2609-7125
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Underwater Target Noise Recognition and Classification Technology based on Multi-Classes Feature Fusion

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