Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise

The direction-of-arrival (DOA) tracking of underwater targets is an important research topic in sonar signal processing. Considering that the underwater DOA tracking is a typical multi-target problem under unknown underwater environment with missing detection, false alarm, and uncertain measurement...
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Gespeichert in:

Autor*in:	Boxuan Zhang [verfasserIn] Xianghao Hou [verfasserIn] Yixin Yang [verfasserIn] Jianbo Zhou [verfasserIn] Shengli Xu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	underwater multi-target direction-of-arrival tracking cardinalized probability hypothesis density filter uncertain measurement noise variational Bayesian approach adaptive tracking

Übergeordnetes Werk:	In: Frontiers in Physics - Frontiers Media S.A., 2014, 11(2023)
Übergeordnetes Werk:	volume:11 ; year:2023

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fphy.2023.1142400

Katalog-ID:	DOAJ088463001

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allfields	10.3389/fphy.2023.1142400 doi (DE-627)DOAJ088463001 (DE-599)DOAJ2186354f7bad4e0a8668d3fef72c799c DE-627 ger DE-627 rakwb eng QC1-999 Boxuan Zhang verfasserin aut Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The direction-of-arrival (DOA) tracking of underwater targets is an important research topic in sonar signal processing. Considering that the underwater DOA tracking is a typical multi-target problem under unknown underwater environment with missing detection, false alarm, and uncertain measurement noise, a robust underwater multi-target DOA tracking method for uncertain measurement noise is proposed. First, a kinematic model of the multiple underwater targets and bearing angle measurement model with missing detection and false alarms are established. Then, the multi-target DOA tracking algorithm is derived by using the cardinalized probability hypothesis density (CPHD) filter, the performance of which largely depends on the accuracy of the parameter of measurement noise variance. In addition, the variational Bayesian approach is used to adaptively estimate the uncertain measurement of noise variance for each measurement of target in the real time of tracking. Thus, the robust underwater multi-target DOA tracking is carried out. Finally, comprehensive experimental validations and discussions are made to prove that the proposed algorithm can provide robust DOA tracking in the multi-target tracking scenario with uncertain measurement noise. underwater multi-target direction-of-arrival tracking cardinalized probability hypothesis density filter uncertain measurement noise variational Bayesian approach adaptive tracking Physics Boxuan Zhang verfasserin aut Xianghao Hou verfasserin aut Xianghao Hou verfasserin aut Yixin Yang verfasserin aut Yixin Yang verfasserin aut Jianbo Zhou verfasserin aut Jianbo Zhou verfasserin aut Shengli Xu verfasserin aut Shengli Xu verfasserin aut In Frontiers in Physics Frontiers Media S.A., 2014 11(2023) (DE-627)750371749 (DE-600)2721033-9 2296424X nnns volume:11 year:2023 https://doi.org/10.3389/fphy.2023.1142400 kostenfrei https://doaj.org/article/2186354f7bad4e0a8668d3fef72c799c kostenfrei https://www.frontiersin.org/articles/10.3389/fphy.2023.1142400/full kostenfrei https://doaj.org/toc/2296-424X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_2003 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 11 2023
spelling	10.3389/fphy.2023.1142400 doi (DE-627)DOAJ088463001 (DE-599)DOAJ2186354f7bad4e0a8668d3fef72c799c DE-627 ger DE-627 rakwb eng QC1-999 Boxuan Zhang verfasserin aut Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The direction-of-arrival (DOA) tracking of underwater targets is an important research topic in sonar signal processing. Considering that the underwater DOA tracking is a typical multi-target problem under unknown underwater environment with missing detection, false alarm, and uncertain measurement noise, a robust underwater multi-target DOA tracking method for uncertain measurement noise is proposed. First, a kinematic model of the multiple underwater targets and bearing angle measurement model with missing detection and false alarms are established. Then, the multi-target DOA tracking algorithm is derived by using the cardinalized probability hypothesis density (CPHD) filter, the performance of which largely depends on the accuracy of the parameter of measurement noise variance. In addition, the variational Bayesian approach is used to adaptively estimate the uncertain measurement of noise variance for each measurement of target in the real time of tracking. Thus, the robust underwater multi-target DOA tracking is carried out. Finally, comprehensive experimental validations and discussions are made to prove that the proposed algorithm can provide robust DOA tracking in the multi-target tracking scenario with uncertain measurement noise. underwater multi-target direction-of-arrival tracking cardinalized probability hypothesis density filter uncertain measurement noise variational Bayesian approach adaptive tracking Physics Boxuan Zhang verfasserin aut Xianghao Hou verfasserin aut Xianghao Hou verfasserin aut Yixin Yang verfasserin aut Yixin Yang verfasserin aut Jianbo Zhou verfasserin aut Jianbo Zhou verfasserin aut Shengli Xu verfasserin aut Shengli Xu verfasserin aut In Frontiers in Physics Frontiers Media S.A., 2014 11(2023) (DE-627)750371749 (DE-600)2721033-9 2296424X nnns volume:11 year:2023 https://doi.org/10.3389/fphy.2023.1142400 kostenfrei https://doaj.org/article/2186354f7bad4e0a8668d3fef72c799c kostenfrei https://www.frontiersin.org/articles/10.3389/fphy.2023.1142400/full kostenfrei https://doaj.org/toc/2296-424X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_2003 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 11 2023
allfields_unstemmed	10.3389/fphy.2023.1142400 doi (DE-627)DOAJ088463001 (DE-599)DOAJ2186354f7bad4e0a8668d3fef72c799c DE-627 ger DE-627 rakwb eng QC1-999 Boxuan Zhang verfasserin aut Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The direction-of-arrival (DOA) tracking of underwater targets is an important research topic in sonar signal processing. Considering that the underwater DOA tracking is a typical multi-target problem under unknown underwater environment with missing detection, false alarm, and uncertain measurement noise, a robust underwater multi-target DOA tracking method for uncertain measurement noise is proposed. First, a kinematic model of the multiple underwater targets and bearing angle measurement model with missing detection and false alarms are established. Then, the multi-target DOA tracking algorithm is derived by using the cardinalized probability hypothesis density (CPHD) filter, the performance of which largely depends on the accuracy of the parameter of measurement noise variance. In addition, the variational Bayesian approach is used to adaptively estimate the uncertain measurement of noise variance for each measurement of target in the real time of tracking. Thus, the robust underwater multi-target DOA tracking is carried out. Finally, comprehensive experimental validations and discussions are made to prove that the proposed algorithm can provide robust DOA tracking in the multi-target tracking scenario with uncertain measurement noise. underwater multi-target direction-of-arrival tracking cardinalized probability hypothesis density filter uncertain measurement noise variational Bayesian approach adaptive tracking Physics Boxuan Zhang verfasserin aut Xianghao Hou verfasserin aut Xianghao Hou verfasserin aut Yixin Yang verfasserin aut Yixin Yang verfasserin aut Jianbo Zhou verfasserin aut Jianbo Zhou verfasserin aut Shengli Xu verfasserin aut Shengli Xu verfasserin aut In Frontiers in Physics Frontiers Media S.A., 2014 11(2023) (DE-627)750371749 (DE-600)2721033-9 2296424X nnns volume:11 year:2023 https://doi.org/10.3389/fphy.2023.1142400 kostenfrei https://doaj.org/article/2186354f7bad4e0a8668d3fef72c799c kostenfrei https://www.frontiersin.org/articles/10.3389/fphy.2023.1142400/full kostenfrei https://doaj.org/toc/2296-424X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_2003 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 11 2023
allfieldsGer	10.3389/fphy.2023.1142400 doi (DE-627)DOAJ088463001 (DE-599)DOAJ2186354f7bad4e0a8668d3fef72c799c DE-627 ger DE-627 rakwb eng QC1-999 Boxuan Zhang verfasserin aut Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The direction-of-arrival (DOA) tracking of underwater targets is an important research topic in sonar signal processing. Considering that the underwater DOA tracking is a typical multi-target problem under unknown underwater environment with missing detection, false alarm, and uncertain measurement noise, a robust underwater multi-target DOA tracking method for uncertain measurement noise is proposed. First, a kinematic model of the multiple underwater targets and bearing angle measurement model with missing detection and false alarms are established. Then, the multi-target DOA tracking algorithm is derived by using the cardinalized probability hypothesis density (CPHD) filter, the performance of which largely depends on the accuracy of the parameter of measurement noise variance. In addition, the variational Bayesian approach is used to adaptively estimate the uncertain measurement of noise variance for each measurement of target in the real time of tracking. Thus, the robust underwater multi-target DOA tracking is carried out. Finally, comprehensive experimental validations and discussions are made to prove that the proposed algorithm can provide robust DOA tracking in the multi-target tracking scenario with uncertain measurement noise. underwater multi-target direction-of-arrival tracking cardinalized probability hypothesis density filter uncertain measurement noise variational Bayesian approach adaptive tracking Physics Boxuan Zhang verfasserin aut Xianghao Hou verfasserin aut Xianghao Hou verfasserin aut Yixin Yang verfasserin aut Yixin Yang verfasserin aut Jianbo Zhou verfasserin aut Jianbo Zhou verfasserin aut Shengli Xu verfasserin aut Shengli Xu verfasserin aut In Frontiers in Physics Frontiers Media S.A., 2014 11(2023) (DE-627)750371749 (DE-600)2721033-9 2296424X nnns volume:11 year:2023 https://doi.org/10.3389/fphy.2023.1142400 kostenfrei https://doaj.org/article/2186354f7bad4e0a8668d3fef72c799c kostenfrei https://www.frontiersin.org/articles/10.3389/fphy.2023.1142400/full kostenfrei https://doaj.org/toc/2296-424X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_2003 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 11 2023
allfieldsSound	10.3389/fphy.2023.1142400 doi (DE-627)DOAJ088463001 (DE-599)DOAJ2186354f7bad4e0a8668d3fef72c799c DE-627 ger DE-627 rakwb eng QC1-999 Boxuan Zhang verfasserin aut Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The direction-of-arrival (DOA) tracking of underwater targets is an important research topic in sonar signal processing. Considering that the underwater DOA tracking is a typical multi-target problem under unknown underwater environment with missing detection, false alarm, and uncertain measurement noise, a robust underwater multi-target DOA tracking method for uncertain measurement noise is proposed. First, a kinematic model of the multiple underwater targets and bearing angle measurement model with missing detection and false alarms are established. Then, the multi-target DOA tracking algorithm is derived by using the cardinalized probability hypothesis density (CPHD) filter, the performance of which largely depends on the accuracy of the parameter of measurement noise variance. In addition, the variational Bayesian approach is used to adaptively estimate the uncertain measurement of noise variance for each measurement of target in the real time of tracking. Thus, the robust underwater multi-target DOA tracking is carried out. Finally, comprehensive experimental validations and discussions are made to prove that the proposed algorithm can provide robust DOA tracking in the multi-target tracking scenario with uncertain measurement noise. underwater multi-target direction-of-arrival tracking cardinalized probability hypothesis density filter uncertain measurement noise variational Bayesian approach adaptive tracking Physics Boxuan Zhang verfasserin aut Xianghao Hou verfasserin aut Xianghao Hou verfasserin aut Yixin Yang verfasserin aut Yixin Yang verfasserin aut Jianbo Zhou verfasserin aut Jianbo Zhou verfasserin aut Shengli Xu verfasserin aut Shengli Xu verfasserin aut In Frontiers in Physics Frontiers Media S.A., 2014 11(2023) (DE-627)750371749 (DE-600)2721033-9 2296424X nnns volume:11 year:2023 https://doi.org/10.3389/fphy.2023.1142400 kostenfrei https://doaj.org/article/2186354f7bad4e0a8668d3fef72c799c kostenfrei https://www.frontiersin.org/articles/10.3389/fphy.2023.1142400/full kostenfrei https://doaj.org/toc/2296-424X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 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_2003 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 11 2023
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authorswithroles_txt_mv	Boxuan Zhang @@aut@@ Xianghao Hou @@aut@@ Yixin Yang @@aut@@ Jianbo Zhou @@aut@@ Shengli Xu @@aut@@
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callnumber-first	Q - Science
author	Boxuan Zhang
spellingShingle	Boxuan Zhang misc QC1-999 misc underwater multi-target direction-of-arrival tracking misc cardinalized probability hypothesis density filter misc uncertain measurement noise misc variational Bayesian approach misc adaptive tracking misc Physics Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise
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topic_title	QC1-999 Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise underwater multi-target direction-of-arrival tracking cardinalized probability hypothesis density filter uncertain measurement noise variational Bayesian approach adaptive tracking
topic	misc QC1-999 misc underwater multi-target direction-of-arrival tracking misc cardinalized probability hypothesis density filter misc uncertain measurement noise misc variational Bayesian approach misc adaptive tracking misc Physics
topic_unstemmed	misc QC1-999 misc underwater multi-target direction-of-arrival tracking misc cardinalized probability hypothesis density filter misc uncertain measurement noise misc variational Bayesian approach misc adaptive tracking misc Physics
topic_browse	misc QC1-999 misc underwater multi-target direction-of-arrival tracking misc cardinalized probability hypothesis density filter misc uncertain measurement noise misc variational Bayesian approach misc adaptive tracking misc Physics
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title	Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise
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title_full	Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise
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journal	Frontiers in Physics
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title_sort	variational bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise
callnumber	QC1-999
title_auth	Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise
abstract	The direction-of-arrival (DOA) tracking of underwater targets is an important research topic in sonar signal processing. Considering that the underwater DOA tracking is a typical multi-target problem under unknown underwater environment with missing detection, false alarm, and uncertain measurement noise, a robust underwater multi-target DOA tracking method for uncertain measurement noise is proposed. First, a kinematic model of the multiple underwater targets and bearing angle measurement model with missing detection and false alarms are established. Then, the multi-target DOA tracking algorithm is derived by using the cardinalized probability hypothesis density (CPHD) filter, the performance of which largely depends on the accuracy of the parameter of measurement noise variance. In addition, the variational Bayesian approach is used to adaptively estimate the uncertain measurement of noise variance for each measurement of target in the real time of tracking. Thus, the robust underwater multi-target DOA tracking is carried out. Finally, comprehensive experimental validations and discussions are made to prove that the proposed algorithm can provide robust DOA tracking in the multi-target tracking scenario with uncertain measurement noise.
abstractGer	The direction-of-arrival (DOA) tracking of underwater targets is an important research topic in sonar signal processing. Considering that the underwater DOA tracking is a typical multi-target problem under unknown underwater environment with missing detection, false alarm, and uncertain measurement noise, a robust underwater multi-target DOA tracking method for uncertain measurement noise is proposed. First, a kinematic model of the multiple underwater targets and bearing angle measurement model with missing detection and false alarms are established. Then, the multi-target DOA tracking algorithm is derived by using the cardinalized probability hypothesis density (CPHD) filter, the performance of which largely depends on the accuracy of the parameter of measurement noise variance. In addition, the variational Bayesian approach is used to adaptively estimate the uncertain measurement of noise variance for each measurement of target in the real time of tracking. Thus, the robust underwater multi-target DOA tracking is carried out. Finally, comprehensive experimental validations and discussions are made to prove that the proposed algorithm can provide robust DOA tracking in the multi-target tracking scenario with uncertain measurement noise.
abstract_unstemmed	The direction-of-arrival (DOA) tracking of underwater targets is an important research topic in sonar signal processing. Considering that the underwater DOA tracking is a typical multi-target problem under unknown underwater environment with missing detection, false alarm, and uncertain measurement noise, a robust underwater multi-target DOA tracking method for uncertain measurement noise is proposed. First, a kinematic model of the multiple underwater targets and bearing angle measurement model with missing detection and false alarms are established. Then, the multi-target DOA tracking algorithm is derived by using the cardinalized probability hypothesis density (CPHD) filter, the performance of which largely depends on the accuracy of the parameter of measurement noise variance. In addition, the variational Bayesian approach is used to adaptively estimate the uncertain measurement of noise variance for each measurement of target in the real time of tracking. Thus, the robust underwater multi-target DOA tracking is carried out. Finally, comprehensive experimental validations and discussions are made to prove that the proposed algorithm can provide robust DOA tracking in the multi-target tracking scenario with uncertain measurement noise.
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title_short	Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise
url	https://doi.org/10.3389/fphy.2023.1142400 https://doaj.org/article/2186354f7bad4e0a8668d3fef72c799c https://www.frontiersin.org/articles/10.3389/fphy.2023.1142400/full https://doaj.org/toc/2296-424X
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author2	Boxuan Zhang Xianghao Hou Yixin Yang Jianbo Zhou Shengli Xu
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doi_str	10.3389/fphy.2023.1142400
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up_date	2024-07-03T17:48:43.968Z
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Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise

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