BLUE filter with fused range estimation

The non-linear filter is subject to divergence for close-range target tracking; an adaptive best linear unbiased estimation (BLUE) filter with fused range estimation is presented. After proposing the notion of predicted range, the weighted estimation of the predicted and measured ranges is used in t...
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Gespeichert in:

Autor*in:	Sheng Hu [verfasserIn] Zhao Wen-bo [verfasserIn] Tang Si-yuan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	filtering theory target tracking fused range estimation predicted range weighted estimation predicted measured ranges converted measurement model rayleigh range measurement adaptive blue filter nonlinear filter close-range target tracking adaptive best linear unbiased estimation filter

Übergeordnetes Werk:	In: The Journal of Engineering - Wiley, 2013, (2019)
Übergeordnetes Werk:	year:2019

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1049/joe.2019.0685

Katalog-ID:	DOAJ074016148

Internformat


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245	1	0	\|a BLUE filter with fused range estimation
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520			\|a The non-linear filter is subject to divergence for close-range target tracking; an adaptive best linear unbiased estimation (BLUE) filter with fused range estimation is presented. After proposing the notion of predicted range, the weighted estimation of the predicted and measured ranges is used in the converted measurement model to track the target. The adaptive BLUE filtering parameters with Rayleigh range measurement are derived. Simulation results show the adaptive BLUE filter exhibits better robustness and accuracy with modest computational burden.
650		4	\|a filtering theory
650		4	\|a target tracking
650		4	\|a fused range estimation
650		4	\|a predicted range
650		4	\|a weighted estimation
650		4	\|a predicted measured ranges
650		4	\|a converted measurement model
650		4	\|a rayleigh range measurement
650		4	\|a adaptive blue filter
650		4	\|a nonlinear filter
650		4	\|a close-range target tracking
650		4	\|a adaptive best linear unbiased estimation filter
653		0	\|a Engineering (General). Civil engineering (General)
700	0		\|a Zhao Wen-bo \|e verfasserin \|4 aut
700	0		\|a Tang Si-yuan \|e verfasserin \|4 aut
773	0	8	\|i In \|t The Journal of Engineering \|d Wiley, 2013 \|g (2019) \|w (DE-627)75682270X \|w (DE-600)2727074-9 \|x 20513305 \|7 nnns
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912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2144
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912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
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912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
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912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4336
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|j 2019

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publishDate	2019
allfields	10.1049/joe.2019.0685 doi (DE-627)DOAJ074016148 (DE-599)DOAJd33f2be005ec4d218c8a9129f7fa5e37 DE-627 ger DE-627 rakwb eng TA1-2040 Sheng Hu verfasserin aut BLUE filter with fused range estimation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The non-linear filter is subject to divergence for close-range target tracking; an adaptive best linear unbiased estimation (BLUE) filter with fused range estimation is presented. After proposing the notion of predicted range, the weighted estimation of the predicted and measured ranges is used in the converted measurement model to track the target. The adaptive BLUE filtering parameters with Rayleigh range measurement are derived. Simulation results show the adaptive BLUE filter exhibits better robustness and accuracy with modest computational burden. filtering theory target tracking fused range estimation predicted range weighted estimation predicted measured ranges converted measurement model rayleigh range measurement adaptive blue filter nonlinear filter close-range target tracking adaptive best linear unbiased estimation filter Engineering (General). Civil engineering (General) Zhao Wen-bo verfasserin aut Tang Si-yuan verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2019.0685 kostenfrei https://doaj.org/article/d33f2be005ec4d218c8a9129f7fa5e37 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0685 kostenfrei https://doaj.org/toc/2051-3305 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
spelling	10.1049/joe.2019.0685 doi (DE-627)DOAJ074016148 (DE-599)DOAJd33f2be005ec4d218c8a9129f7fa5e37 DE-627 ger DE-627 rakwb eng TA1-2040 Sheng Hu verfasserin aut BLUE filter with fused range estimation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The non-linear filter is subject to divergence for close-range target tracking; an adaptive best linear unbiased estimation (BLUE) filter with fused range estimation is presented. After proposing the notion of predicted range, the weighted estimation of the predicted and measured ranges is used in the converted measurement model to track the target. The adaptive BLUE filtering parameters with Rayleigh range measurement are derived. Simulation results show the adaptive BLUE filter exhibits better robustness and accuracy with modest computational burden. filtering theory target tracking fused range estimation predicted range weighted estimation predicted measured ranges converted measurement model rayleigh range measurement adaptive blue filter nonlinear filter close-range target tracking adaptive best linear unbiased estimation filter Engineering (General). Civil engineering (General) Zhao Wen-bo verfasserin aut Tang Si-yuan verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2019.0685 kostenfrei https://doaj.org/article/d33f2be005ec4d218c8a9129f7fa5e37 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0685 kostenfrei https://doaj.org/toc/2051-3305 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
allfields_unstemmed	10.1049/joe.2019.0685 doi (DE-627)DOAJ074016148 (DE-599)DOAJd33f2be005ec4d218c8a9129f7fa5e37 DE-627 ger DE-627 rakwb eng TA1-2040 Sheng Hu verfasserin aut BLUE filter with fused range estimation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The non-linear filter is subject to divergence for close-range target tracking; an adaptive best linear unbiased estimation (BLUE) filter with fused range estimation is presented. After proposing the notion of predicted range, the weighted estimation of the predicted and measured ranges is used in the converted measurement model to track the target. The adaptive BLUE filtering parameters with Rayleigh range measurement are derived. Simulation results show the adaptive BLUE filter exhibits better robustness and accuracy with modest computational burden. filtering theory target tracking fused range estimation predicted range weighted estimation predicted measured ranges converted measurement model rayleigh range measurement adaptive blue filter nonlinear filter close-range target tracking adaptive best linear unbiased estimation filter Engineering (General). Civil engineering (General) Zhao Wen-bo verfasserin aut Tang Si-yuan verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2019.0685 kostenfrei https://doaj.org/article/d33f2be005ec4d218c8a9129f7fa5e37 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0685 kostenfrei https://doaj.org/toc/2051-3305 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
allfieldsGer	10.1049/joe.2019.0685 doi (DE-627)DOAJ074016148 (DE-599)DOAJd33f2be005ec4d218c8a9129f7fa5e37 DE-627 ger DE-627 rakwb eng TA1-2040 Sheng Hu verfasserin aut BLUE filter with fused range estimation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The non-linear filter is subject to divergence for close-range target tracking; an adaptive best linear unbiased estimation (BLUE) filter with fused range estimation is presented. After proposing the notion of predicted range, the weighted estimation of the predicted and measured ranges is used in the converted measurement model to track the target. The adaptive BLUE filtering parameters with Rayleigh range measurement are derived. Simulation results show the adaptive BLUE filter exhibits better robustness and accuracy with modest computational burden. filtering theory target tracking fused range estimation predicted range weighted estimation predicted measured ranges converted measurement model rayleigh range measurement adaptive blue filter nonlinear filter close-range target tracking adaptive best linear unbiased estimation filter Engineering (General). Civil engineering (General) Zhao Wen-bo verfasserin aut Tang Si-yuan verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2019.0685 kostenfrei https://doaj.org/article/d33f2be005ec4d218c8a9129f7fa5e37 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0685 kostenfrei https://doaj.org/toc/2051-3305 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
allfieldsSound	10.1049/joe.2019.0685 doi (DE-627)DOAJ074016148 (DE-599)DOAJd33f2be005ec4d218c8a9129f7fa5e37 DE-627 ger DE-627 rakwb eng TA1-2040 Sheng Hu verfasserin aut BLUE filter with fused range estimation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The non-linear filter is subject to divergence for close-range target tracking; an adaptive best linear unbiased estimation (BLUE) filter with fused range estimation is presented. After proposing the notion of predicted range, the weighted estimation of the predicted and measured ranges is used in the converted measurement model to track the target. The adaptive BLUE filtering parameters with Rayleigh range measurement are derived. Simulation results show the adaptive BLUE filter exhibits better robustness and accuracy with modest computational burden. filtering theory target tracking fused range estimation predicted range weighted estimation predicted measured ranges converted measurement model rayleigh range measurement adaptive blue filter nonlinear filter close-range target tracking adaptive best linear unbiased estimation filter Engineering (General). Civil engineering (General) Zhao Wen-bo verfasserin aut Tang Si-yuan verfasserin aut In The Journal of Engineering Wiley, 2013 (2019) (DE-627)75682270X (DE-600)2727074-9 20513305 nnns year:2019 https://doi.org/10.1049/joe.2019.0685 kostenfrei https://doaj.org/article/d33f2be005ec4d218c8a9129f7fa5e37 kostenfrei https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0685 kostenfrei https://doaj.org/toc/2051-3305 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_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2019
language	English
source	In The Journal of Engineering (2019) year:2019
sourceStr	In The Journal of Engineering (2019) year:2019
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	filtering theory target tracking fused range estimation predicted range weighted estimation predicted measured ranges converted measurement model rayleigh range measurement adaptive blue filter nonlinear filter close-range target tracking adaptive best linear unbiased estimation filter Engineering (General). Civil engineering (General)
isfreeaccess_bool	true
container_title	The Journal of Engineering
authorswithroles_txt_mv	Sheng Hu @@aut@@ Zhao Wen-bo @@aut@@ Tang Si-yuan @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	75682270X
id	DOAJ074016148
language_de	englisch
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callnumber-first	T - Technology
author	Sheng Hu
spellingShingle	Sheng Hu misc TA1-2040 misc filtering theory misc target tracking misc fused range estimation misc predicted range misc weighted estimation misc predicted measured ranges misc converted measurement model misc rayleigh range measurement misc adaptive blue filter misc nonlinear filter misc close-range target tracking misc adaptive best linear unbiased estimation filter misc Engineering (General). Civil engineering (General) BLUE filter with fused range estimation
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topic_title	TA1-2040 BLUE filter with fused range estimation filtering theory target tracking fused range estimation predicted range weighted estimation predicted measured ranges converted measurement model rayleigh range measurement adaptive blue filter nonlinear filter close-range target tracking adaptive best linear unbiased estimation filter
topic	misc TA1-2040 misc filtering theory misc target tracking misc fused range estimation misc predicted range misc weighted estimation misc predicted measured ranges misc converted measurement model misc rayleigh range measurement misc adaptive blue filter misc nonlinear filter misc close-range target tracking misc adaptive best linear unbiased estimation filter misc Engineering (General). Civil engineering (General)
topic_unstemmed	misc TA1-2040 misc filtering theory misc target tracking misc fused range estimation misc predicted range misc weighted estimation misc predicted measured ranges misc converted measurement model misc rayleigh range measurement misc adaptive blue filter misc nonlinear filter misc close-range target tracking misc adaptive best linear unbiased estimation filter misc Engineering (General). Civil engineering (General)
topic_browse	misc TA1-2040 misc filtering theory misc target tracking misc fused range estimation misc predicted range misc weighted estimation misc predicted measured ranges misc converted measurement model misc rayleigh range measurement misc adaptive blue filter misc nonlinear filter misc close-range target tracking misc adaptive best linear unbiased estimation filter misc Engineering (General). Civil engineering (General)
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title	BLUE filter with fused range estimation
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title_full	BLUE filter with fused range estimation
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title_sort	blue filter with fused range estimation
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title_auth	BLUE filter with fused range estimation
abstract	The non-linear filter is subject to divergence for close-range target tracking; an adaptive best linear unbiased estimation (BLUE) filter with fused range estimation is presented. After proposing the notion of predicted range, the weighted estimation of the predicted and measured ranges is used in the converted measurement model to track the target. The adaptive BLUE filtering parameters with Rayleigh range measurement are derived. Simulation results show the adaptive BLUE filter exhibits better robustness and accuracy with modest computational burden.
abstractGer	The non-linear filter is subject to divergence for close-range target tracking; an adaptive best linear unbiased estimation (BLUE) filter with fused range estimation is presented. After proposing the notion of predicted range, the weighted estimation of the predicted and measured ranges is used in the converted measurement model to track the target. The adaptive BLUE filtering parameters with Rayleigh range measurement are derived. Simulation results show the adaptive BLUE filter exhibits better robustness and accuracy with modest computational burden.
abstract_unstemmed	The non-linear filter is subject to divergence for close-range target tracking; an adaptive best linear unbiased estimation (BLUE) filter with fused range estimation is presented. After proposing the notion of predicted range, the weighted estimation of the predicted and measured ranges is used in the converted measurement model to track the target. The adaptive BLUE filtering parameters with Rayleigh range measurement are derived. Simulation results show the adaptive BLUE filter exhibits better robustness and accuracy with modest computational burden.
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title_short	BLUE filter with fused range estimation
url	https://doi.org/10.1049/joe.2019.0685 https://doaj.org/article/d33f2be005ec4d218c8a9129f7fa5e37 https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0685 https://doaj.org/toc/2051-3305
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up_date	2024-07-03T20:52:22.559Z
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score	7.400467

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BLUE filter with fused range estimation

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