Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter

This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. Th...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Sufyan Ali Memon [verfasserIn] Min-Seuk Park [verfasserIn] Imran Memon [verfasserIn] Wan-Gu Kim [verfasserIn] Sajid Khan [verfasserIn] Yifang Shi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	component existence probabilities false-track discrimination multi-maneuvering-targets smoothing target existence probabilities

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 22(2022), 13, p 4759
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:13, p 4759

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s22134759

Katalog-ID:	DOAJ025261258

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allfields	10.3390/s22134759 doi (DE-627)DOAJ025261258 (DE-599)DOAJ32c9dc23f94349ebbbf28c55d883f566 DE-627 ger DE-627 rakwb eng TP1-1185 Sufyan Ali Memon verfasserin aut Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to as smoothing MMT using the JITS (MMT-sJITS). The existing FIsJITS algorithm is computationally more complex to solve for the MMT situation because it enumerates a substantial number of measurement-to-track assignments and calculates their posteriori probabilities globally. The MMT-sJITS updates a current target track by assuming the joint (common) measurements detected by neighbor tracks are modified clutters (or pretended spurious measurements). Thus, target measurement concealed by a joint measurement is optimally estimated based on measurement density of the modified clutter. This reduces computational complexity and provides improved tracking performance. The MMT-sJITS generates forward tracks and backward tracks using the measurements collected by a sensor such as a radar. The forward and backward multi-tracks state predictions are fused to obtain priori smoothing multi-track state prediction, as well as their component existence probabilities. This calculates the smoothing estimate required to compute the forward JITS state estimate, which reinforces the MMT tracking efficiently. Monte Carlo simulation is used to verify best false-track discrimination (FTD) analysis in comparison with existing multi-targets tracking algorithms. component existence probabilities false-track discrimination multi-maneuvering-targets smoothing target existence probabilities Chemical technology Min-Seuk Park verfasserin aut Imran Memon verfasserin aut Wan-Gu Kim verfasserin aut Sajid Khan verfasserin aut Yifang Shi verfasserin aut In Sensors MDPI AG, 2003 22(2022), 13, p 4759 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:13, p 4759 https://doi.org/10.3390/s22134759 kostenfrei https://doaj.org/article/32c9dc23f94349ebbbf28c55d883f566 kostenfrei https://www.mdpi.com/1424-8220/22/13/4759 kostenfrei https://doaj.org/toc/1424-8220 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_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 22 2022 13, p 4759
spelling	10.3390/s22134759 doi (DE-627)DOAJ025261258 (DE-599)DOAJ32c9dc23f94349ebbbf28c55d883f566 DE-627 ger DE-627 rakwb eng TP1-1185 Sufyan Ali Memon verfasserin aut Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to as smoothing MMT using the JITS (MMT-sJITS). The existing FIsJITS algorithm is computationally more complex to solve for the MMT situation because it enumerates a substantial number of measurement-to-track assignments and calculates their posteriori probabilities globally. The MMT-sJITS updates a current target track by assuming the joint (common) measurements detected by neighbor tracks are modified clutters (or pretended spurious measurements). Thus, target measurement concealed by a joint measurement is optimally estimated based on measurement density of the modified clutter. This reduces computational complexity and provides improved tracking performance. The MMT-sJITS generates forward tracks and backward tracks using the measurements collected by a sensor such as a radar. The forward and backward multi-tracks state predictions are fused to obtain priori smoothing multi-track state prediction, as well as their component existence probabilities. This calculates the smoothing estimate required to compute the forward JITS state estimate, which reinforces the MMT tracking efficiently. Monte Carlo simulation is used to verify best false-track discrimination (FTD) analysis in comparison with existing multi-targets tracking algorithms. component existence probabilities false-track discrimination multi-maneuvering-targets smoothing target existence probabilities Chemical technology Min-Seuk Park verfasserin aut Imran Memon verfasserin aut Wan-Gu Kim verfasserin aut Sajid Khan verfasserin aut Yifang Shi verfasserin aut In Sensors MDPI AG, 2003 22(2022), 13, p 4759 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:13, p 4759 https://doi.org/10.3390/s22134759 kostenfrei https://doaj.org/article/32c9dc23f94349ebbbf28c55d883f566 kostenfrei https://www.mdpi.com/1424-8220/22/13/4759 kostenfrei https://doaj.org/toc/1424-8220 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_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 22 2022 13, p 4759
allfields_unstemmed	10.3390/s22134759 doi (DE-627)DOAJ025261258 (DE-599)DOAJ32c9dc23f94349ebbbf28c55d883f566 DE-627 ger DE-627 rakwb eng TP1-1185 Sufyan Ali Memon verfasserin aut Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to as smoothing MMT using the JITS (MMT-sJITS). The existing FIsJITS algorithm is computationally more complex to solve for the MMT situation because it enumerates a substantial number of measurement-to-track assignments and calculates their posteriori probabilities globally. The MMT-sJITS updates a current target track by assuming the joint (common) measurements detected by neighbor tracks are modified clutters (or pretended spurious measurements). Thus, target measurement concealed by a joint measurement is optimally estimated based on measurement density of the modified clutter. This reduces computational complexity and provides improved tracking performance. The MMT-sJITS generates forward tracks and backward tracks using the measurements collected by a sensor such as a radar. The forward and backward multi-tracks state predictions are fused to obtain priori smoothing multi-track state prediction, as well as their component existence probabilities. This calculates the smoothing estimate required to compute the forward JITS state estimate, which reinforces the MMT tracking efficiently. Monte Carlo simulation is used to verify best false-track discrimination (FTD) analysis in comparison with existing multi-targets tracking algorithms. component existence probabilities false-track discrimination multi-maneuvering-targets smoothing target existence probabilities Chemical technology Min-Seuk Park verfasserin aut Imran Memon verfasserin aut Wan-Gu Kim verfasserin aut Sajid Khan verfasserin aut Yifang Shi verfasserin aut In Sensors MDPI AG, 2003 22(2022), 13, p 4759 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:13, p 4759 https://doi.org/10.3390/s22134759 kostenfrei https://doaj.org/article/32c9dc23f94349ebbbf28c55d883f566 kostenfrei https://www.mdpi.com/1424-8220/22/13/4759 kostenfrei https://doaj.org/toc/1424-8220 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_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 22 2022 13, p 4759
allfieldsGer	10.3390/s22134759 doi (DE-627)DOAJ025261258 (DE-599)DOAJ32c9dc23f94349ebbbf28c55d883f566 DE-627 ger DE-627 rakwb eng TP1-1185 Sufyan Ali Memon verfasserin aut Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to as smoothing MMT using the JITS (MMT-sJITS). The existing FIsJITS algorithm is computationally more complex to solve for the MMT situation because it enumerates a substantial number of measurement-to-track assignments and calculates their posteriori probabilities globally. The MMT-sJITS updates a current target track by assuming the joint (common) measurements detected by neighbor tracks are modified clutters (or pretended spurious measurements). Thus, target measurement concealed by a joint measurement is optimally estimated based on measurement density of the modified clutter. This reduces computational complexity and provides improved tracking performance. The MMT-sJITS generates forward tracks and backward tracks using the measurements collected by a sensor such as a radar. The forward and backward multi-tracks state predictions are fused to obtain priori smoothing multi-track state prediction, as well as their component existence probabilities. This calculates the smoothing estimate required to compute the forward JITS state estimate, which reinforces the MMT tracking efficiently. Monte Carlo simulation is used to verify best false-track discrimination (FTD) analysis in comparison with existing multi-targets tracking algorithms. component existence probabilities false-track discrimination multi-maneuvering-targets smoothing target existence probabilities Chemical technology Min-Seuk Park verfasserin aut Imran Memon verfasserin aut Wan-Gu Kim verfasserin aut Sajid Khan verfasserin aut Yifang Shi verfasserin aut In Sensors MDPI AG, 2003 22(2022), 13, p 4759 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:13, p 4759 https://doi.org/10.3390/s22134759 kostenfrei https://doaj.org/article/32c9dc23f94349ebbbf28c55d883f566 kostenfrei https://www.mdpi.com/1424-8220/22/13/4759 kostenfrei https://doaj.org/toc/1424-8220 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_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 22 2022 13, p 4759
allfieldsSound	10.3390/s22134759 doi (DE-627)DOAJ025261258 (DE-599)DOAJ32c9dc23f94349ebbbf28c55d883f566 DE-627 ger DE-627 rakwb eng TP1-1185 Sufyan Ali Memon verfasserin aut Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to as smoothing MMT using the JITS (MMT-sJITS). The existing FIsJITS algorithm is computationally more complex to solve for the MMT situation because it enumerates a substantial number of measurement-to-track assignments and calculates their posteriori probabilities globally. The MMT-sJITS updates a current target track by assuming the joint (common) measurements detected by neighbor tracks are modified clutters (or pretended spurious measurements). Thus, target measurement concealed by a joint measurement is optimally estimated based on measurement density of the modified clutter. This reduces computational complexity and provides improved tracking performance. The MMT-sJITS generates forward tracks and backward tracks using the measurements collected by a sensor such as a radar. The forward and backward multi-tracks state predictions are fused to obtain priori smoothing multi-track state prediction, as well as their component existence probabilities. This calculates the smoothing estimate required to compute the forward JITS state estimate, which reinforces the MMT tracking efficiently. Monte Carlo simulation is used to verify best false-track discrimination (FTD) analysis in comparison with existing multi-targets tracking algorithms. component existence probabilities false-track discrimination multi-maneuvering-targets smoothing target existence probabilities Chemical technology Min-Seuk Park verfasserin aut Imran Memon verfasserin aut Wan-Gu Kim verfasserin aut Sajid Khan verfasserin aut Yifang Shi verfasserin aut In Sensors MDPI AG, 2003 22(2022), 13, p 4759 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:13, p 4759 https://doi.org/10.3390/s22134759 kostenfrei https://doaj.org/article/32c9dc23f94349ebbbf28c55d883f566 kostenfrei https://www.mdpi.com/1424-8220/22/13/4759 kostenfrei https://doaj.org/toc/1424-8220 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_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 22 2022 13, p 4759
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callnumber-first	T - Technology
author	Sufyan Ali Memon
spellingShingle	Sufyan Ali Memon misc TP1-1185 misc component existence probabilities misc false-track discrimination misc multi-maneuvering-targets misc smoothing misc target existence probabilities misc Chemical technology Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter
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topic_title	TP1-1185 Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter component existence probabilities false-track discrimination multi-maneuvering-targets smoothing target existence probabilities
topic	misc TP1-1185 misc component existence probabilities misc false-track discrimination misc multi-maneuvering-targets misc smoothing misc target existence probabilities misc Chemical technology
topic_unstemmed	misc TP1-1185 misc component existence probabilities misc false-track discrimination misc multi-maneuvering-targets misc smoothing misc target existence probabilities misc Chemical technology
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title	Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter
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title_full	Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter
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title_sort	modified smoothing algorithm for tracking multiple maneuvering targets in clutter
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title_auth	Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter
abstract	This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to as smoothing MMT using the JITS (MMT-sJITS). The existing FIsJITS algorithm is computationally more complex to solve for the MMT situation because it enumerates a substantial number of measurement-to-track assignments and calculates their posteriori probabilities globally. The MMT-sJITS updates a current target track by assuming the joint (common) measurements detected by neighbor tracks are modified clutters (or pretended spurious measurements). Thus, target measurement concealed by a joint measurement is optimally estimated based on measurement density of the modified clutter. This reduces computational complexity and provides improved tracking performance. The MMT-sJITS generates forward tracks and backward tracks using the measurements collected by a sensor such as a radar. The forward and backward multi-tracks state predictions are fused to obtain priori smoothing multi-track state prediction, as well as their component existence probabilities. This calculates the smoothing estimate required to compute the forward JITS state estimate, which reinforces the MMT tracking efficiently. Monte Carlo simulation is used to verify best false-track discrimination (FTD) analysis in comparison with existing multi-targets tracking algorithms.
abstractGer	This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to as smoothing MMT using the JITS (MMT-sJITS). The existing FIsJITS algorithm is computationally more complex to solve for the MMT situation because it enumerates a substantial number of measurement-to-track assignments and calculates their posteriori probabilities globally. The MMT-sJITS updates a current target track by assuming the joint (common) measurements detected by neighbor tracks are modified clutters (or pretended spurious measurements). Thus, target measurement concealed by a joint measurement is optimally estimated based on measurement density of the modified clutter. This reduces computational complexity and provides improved tracking performance. The MMT-sJITS generates forward tracks and backward tracks using the measurements collected by a sensor such as a radar. The forward and backward multi-tracks state predictions are fused to obtain priori smoothing multi-track state prediction, as well as their component existence probabilities. This calculates the smoothing estimate required to compute the forward JITS state estimate, which reinforces the MMT tracking efficiently. Monte Carlo simulation is used to verify best false-track discrimination (FTD) analysis in comparison with existing multi-targets tracking algorithms.
abstract_unstemmed	This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to as smoothing MMT using the JITS (MMT-sJITS). The existing FIsJITS algorithm is computationally more complex to solve for the MMT situation because it enumerates a substantial number of measurement-to-track assignments and calculates their posteriori probabilities globally. The MMT-sJITS updates a current target track by assuming the joint (common) measurements detected by neighbor tracks are modified clutters (or pretended spurious measurements). Thus, target measurement concealed by a joint measurement is optimally estimated based on measurement density of the modified clutter. This reduces computational complexity and provides improved tracking performance. The MMT-sJITS generates forward tracks and backward tracks using the measurements collected by a sensor such as a radar. The forward and backward multi-tracks state predictions are fused to obtain priori smoothing multi-track state prediction, as well as their component existence probabilities. This calculates the smoothing estimate required to compute the forward JITS state estimate, which reinforces the MMT tracking efficiently. Monte Carlo simulation is used to verify best false-track discrimination (FTD) analysis in comparison with existing multi-targets tracking algorithms.
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container_issue	13, p 4759
title_short	Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter
url	https://doi.org/10.3390/s22134759 https://doaj.org/article/32c9dc23f94349ebbbf28c55d883f566 https://www.mdpi.com/1424-8220/22/13/4759 https://doaj.org/toc/1424-8220
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author2	Min-Seuk Park Imran Memon Wan-Gu Kim Sajid Khan Yifang Shi
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doi_str	10.3390/s22134759
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up_date	2024-07-03T13:57:03.600Z
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Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter

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