δ-Generalized Labeled Multi-Bernoulli Filter Using Amplitude Information of Neighboring Cells

The amplitude information (AI) of echoed signals plays an important role in radar target detection and tracking. A lot of research shows that the introduction of AI enables the tracking algorithm to distinguish targets from clutter better and then improves the performance of data association. The cu...
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Autor*in:	Chao Liu [verfasserIn] Jinping Sun [verfasserIn] Peng Lei [verfasserIn] Yaolong Qi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	δ-GLMB filter amplitude information neighboring cells multi-target tracking

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 18(2018), 4, p 1153
Übergeordnetes Werk:	volume:18 ; year:2018 ; number:4, p 1153

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s18041153

Katalog-ID:	DOAJ084655992

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520			\|a The amplitude information (AI) of echoed signals plays an important role in radar target detection and tracking. A lot of research shows that the introduction of AI enables the tracking algorithm to distinguish targets from clutter better and then improves the performance of data association. The current AI-aided tracking algorithms only consider the signal amplitude in the range-azimuth cell where measurement exists. However, since radar echoes always contain backscattered signals from multiple cells, the useful information of neighboring cells would be lost if directly applying those existing methods. In order to solve this issue, a new δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is proposed. It exploits the AI of radar echoes from neighboring cells to construct a united amplitude likelihood ratio, and then plugs it into the update process and the measurement-track assignment cost matrix of the δ-GLMB filter. Simulation results show that the proposed approach has better performance in target’s state and number estimation than that of the δ-GLMB only using single-cell AI in low signal-to-clutter-ratio (SCR) environment.
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spelling	10.3390/s18041153 doi (DE-627)DOAJ084655992 (DE-599)DOAJ998eed48cd5d42fca7e99a8aa0ac5467 DE-627 ger DE-627 rakwb eng TP1-1185 Chao Liu verfasserin aut δ-Generalized Labeled Multi-Bernoulli Filter Using Amplitude Information of Neighboring Cells 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The amplitude information (AI) of echoed signals plays an important role in radar target detection and tracking. A lot of research shows that the introduction of AI enables the tracking algorithm to distinguish targets from clutter better and then improves the performance of data association. The current AI-aided tracking algorithms only consider the signal amplitude in the range-azimuth cell where measurement exists. However, since radar echoes always contain backscattered signals from multiple cells, the useful information of neighboring cells would be lost if directly applying those existing methods. In order to solve this issue, a new δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is proposed. It exploits the AI of radar echoes from neighboring cells to construct a united amplitude likelihood ratio, and then plugs it into the update process and the measurement-track assignment cost matrix of the δ-GLMB filter. Simulation results show that the proposed approach has better performance in target’s state and number estimation than that of the δ-GLMB only using single-cell AI in low signal-to-clutter-ratio (SCR) environment. δ-GLMB filter amplitude information neighboring cells multi-target tracking Chemical technology Jinping Sun verfasserin aut Peng Lei verfasserin aut Yaolong Qi verfasserin aut In Sensors MDPI AG, 2003 18(2018), 4, p 1153 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:18 year:2018 number:4, p 1153 https://doi.org/10.3390/s18041153 kostenfrei https://doaj.org/article/998eed48cd5d42fca7e99a8aa0ac5467 kostenfrei http://www.mdpi.com/1424-8220/18/4/1153 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 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 18 2018 4, p 1153
allfields_unstemmed	10.3390/s18041153 doi (DE-627)DOAJ084655992 (DE-599)DOAJ998eed48cd5d42fca7e99a8aa0ac5467 DE-627 ger DE-627 rakwb eng TP1-1185 Chao Liu verfasserin aut δ-Generalized Labeled Multi-Bernoulli Filter Using Amplitude Information of Neighboring Cells 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The amplitude information (AI) of echoed signals plays an important role in radar target detection and tracking. A lot of research shows that the introduction of AI enables the tracking algorithm to distinguish targets from clutter better and then improves the performance of data association. The current AI-aided tracking algorithms only consider the signal amplitude in the range-azimuth cell where measurement exists. However, since radar echoes always contain backscattered signals from multiple cells, the useful information of neighboring cells would be lost if directly applying those existing methods. In order to solve this issue, a new δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is proposed. It exploits the AI of radar echoes from neighboring cells to construct a united amplitude likelihood ratio, and then plugs it into the update process and the measurement-track assignment cost matrix of the δ-GLMB filter. Simulation results show that the proposed approach has better performance in target’s state and number estimation than that of the δ-GLMB only using single-cell AI in low signal-to-clutter-ratio (SCR) environment. δ-GLMB filter amplitude information neighboring cells multi-target tracking Chemical technology Jinping Sun verfasserin aut Peng Lei verfasserin aut Yaolong Qi verfasserin aut In Sensors MDPI AG, 2003 18(2018), 4, p 1153 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:18 year:2018 number:4, p 1153 https://doi.org/10.3390/s18041153 kostenfrei https://doaj.org/article/998eed48cd5d42fca7e99a8aa0ac5467 kostenfrei http://www.mdpi.com/1424-8220/18/4/1153 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 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 18 2018 4, p 1153
allfieldsGer	10.3390/s18041153 doi (DE-627)DOAJ084655992 (DE-599)DOAJ998eed48cd5d42fca7e99a8aa0ac5467 DE-627 ger DE-627 rakwb eng TP1-1185 Chao Liu verfasserin aut δ-Generalized Labeled Multi-Bernoulli Filter Using Amplitude Information of Neighboring Cells 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The amplitude information (AI) of echoed signals plays an important role in radar target detection and tracking. A lot of research shows that the introduction of AI enables the tracking algorithm to distinguish targets from clutter better and then improves the performance of data association. The current AI-aided tracking algorithms only consider the signal amplitude in the range-azimuth cell where measurement exists. However, since radar echoes always contain backscattered signals from multiple cells, the useful information of neighboring cells would be lost if directly applying those existing methods. In order to solve this issue, a new δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is proposed. It exploits the AI of radar echoes from neighboring cells to construct a united amplitude likelihood ratio, and then plugs it into the update process and the measurement-track assignment cost matrix of the δ-GLMB filter. Simulation results show that the proposed approach has better performance in target’s state and number estimation than that of the δ-GLMB only using single-cell AI in low signal-to-clutter-ratio (SCR) environment. δ-GLMB filter amplitude information neighboring cells multi-target tracking Chemical technology Jinping Sun verfasserin aut Peng Lei verfasserin aut Yaolong Qi verfasserin aut In Sensors MDPI AG, 2003 18(2018), 4, p 1153 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:18 year:2018 number:4, p 1153 https://doi.org/10.3390/s18041153 kostenfrei https://doaj.org/article/998eed48cd5d42fca7e99a8aa0ac5467 kostenfrei http://www.mdpi.com/1424-8220/18/4/1153 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 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 18 2018 4, p 1153
allfieldsSound	10.3390/s18041153 doi (DE-627)DOAJ084655992 (DE-599)DOAJ998eed48cd5d42fca7e99a8aa0ac5467 DE-627 ger DE-627 rakwb eng TP1-1185 Chao Liu verfasserin aut δ-Generalized Labeled Multi-Bernoulli Filter Using Amplitude Information of Neighboring Cells 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The amplitude information (AI) of echoed signals plays an important role in radar target detection and tracking. A lot of research shows that the introduction of AI enables the tracking algorithm to distinguish targets from clutter better and then improves the performance of data association. The current AI-aided tracking algorithms only consider the signal amplitude in the range-azimuth cell where measurement exists. However, since radar echoes always contain backscattered signals from multiple cells, the useful information of neighboring cells would be lost if directly applying those existing methods. In order to solve this issue, a new δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is proposed. It exploits the AI of radar echoes from neighboring cells to construct a united amplitude likelihood ratio, and then plugs it into the update process and the measurement-track assignment cost matrix of the δ-GLMB filter. Simulation results show that the proposed approach has better performance in target’s state and number estimation than that of the δ-GLMB only using single-cell AI in low signal-to-clutter-ratio (SCR) environment. δ-GLMB filter amplitude information neighboring cells multi-target tracking Chemical technology Jinping Sun verfasserin aut Peng Lei verfasserin aut Yaolong Qi verfasserin aut In Sensors MDPI AG, 2003 18(2018), 4, p 1153 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:18 year:2018 number:4, p 1153 https://doi.org/10.3390/s18041153 kostenfrei https://doaj.org/article/998eed48cd5d42fca7e99a8aa0ac5467 kostenfrei http://www.mdpi.com/1424-8220/18/4/1153 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 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 18 2018 4, p 1153
language	English
source	In Sensors 18(2018), 4, p 1153 volume:18 year:2018 number:4, p 1153
sourceStr	In Sensors 18(2018), 4, p 1153 volume:18 year:2018 number:4, p 1153
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	δ-GLMB filter amplitude information neighboring cells multi-target tracking Chemical technology
isfreeaccess_bool	true
container_title	Sensors
authorswithroles_txt_mv	Chao Liu @@aut@@ Jinping Sun @@aut@@ Peng Lei @@aut@@ Yaolong Qi @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
hierarchy_top_id	331640910
id	DOAJ084655992
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callnumber-first	T - Technology
author	Chao Liu
spellingShingle	Chao Liu misc TP1-1185 misc δ-GLMB filter misc amplitude information misc neighboring cells misc multi-target tracking misc Chemical technology δ-Generalized Labeled Multi-Bernoulli Filter Using Amplitude Information of Neighboring Cells
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topic_title	TP1-1185 δ-Generalized Labeled Multi-Bernoulli Filter Using Amplitude Information of Neighboring Cells δ-GLMB filter amplitude information neighboring cells multi-target tracking
topic	misc TP1-1185 misc δ-GLMB filter misc amplitude information misc neighboring cells misc multi-target tracking misc Chemical technology
topic_unstemmed	misc TP1-1185 misc δ-GLMB filter misc amplitude information misc neighboring cells misc multi-target tracking misc Chemical technology
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title	δ-Generalized Labeled Multi-Bernoulli Filter Using Amplitude Information of Neighboring Cells
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title_sort	δ-generalized labeled multi-bernoulli filter using amplitude information of neighboring cells
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title_auth	δ-Generalized Labeled Multi-Bernoulli Filter Using Amplitude Information of Neighboring Cells
abstract	The amplitude information (AI) of echoed signals plays an important role in radar target detection and tracking. A lot of research shows that the introduction of AI enables the tracking algorithm to distinguish targets from clutter better and then improves the performance of data association. The current AI-aided tracking algorithms only consider the signal amplitude in the range-azimuth cell where measurement exists. However, since radar echoes always contain backscattered signals from multiple cells, the useful information of neighboring cells would be lost if directly applying those existing methods. In order to solve this issue, a new δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is proposed. It exploits the AI of radar echoes from neighboring cells to construct a united amplitude likelihood ratio, and then plugs it into the update process and the measurement-track assignment cost matrix of the δ-GLMB filter. Simulation results show that the proposed approach has better performance in target’s state and number estimation than that of the δ-GLMB only using single-cell AI in low signal-to-clutter-ratio (SCR) environment.
abstractGer	The amplitude information (AI) of echoed signals plays an important role in radar target detection and tracking. A lot of research shows that the introduction of AI enables the tracking algorithm to distinguish targets from clutter better and then improves the performance of data association. The current AI-aided tracking algorithms only consider the signal amplitude in the range-azimuth cell where measurement exists. However, since radar echoes always contain backscattered signals from multiple cells, the useful information of neighboring cells would be lost if directly applying those existing methods. In order to solve this issue, a new δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is proposed. It exploits the AI of radar echoes from neighboring cells to construct a united amplitude likelihood ratio, and then plugs it into the update process and the measurement-track assignment cost matrix of the δ-GLMB filter. Simulation results show that the proposed approach has better performance in target’s state and number estimation than that of the δ-GLMB only using single-cell AI in low signal-to-clutter-ratio (SCR) environment.
abstract_unstemmed	The amplitude information (AI) of echoed signals plays an important role in radar target detection and tracking. A lot of research shows that the introduction of AI enables the tracking algorithm to distinguish targets from clutter better and then improves the performance of data association. The current AI-aided tracking algorithms only consider the signal amplitude in the range-azimuth cell where measurement exists. However, since radar echoes always contain backscattered signals from multiple cells, the useful information of neighboring cells would be lost if directly applying those existing methods. In order to solve this issue, a new δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is proposed. It exploits the AI of radar echoes from neighboring cells to construct a united amplitude likelihood ratio, and then plugs it into the update process and the measurement-track assignment cost matrix of the δ-GLMB filter. Simulation results show that the proposed approach has better performance in target’s state and number estimation than that of the δ-GLMB only using single-cell AI in low signal-to-clutter-ratio (SCR) environment.
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title_short	δ-Generalized Labeled Multi-Bernoulli Filter Using Amplitude Information of Neighboring Cells
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