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...
Ausführliche Beschreibung
Autor*in: |
Sheng Hu [verfasserIn] Zhao Wen-bo [verfasserIn] Tang Si-yuan [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2019 |
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Schlagwörter: |
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Übergeordnetes Werk: |
In: The Journal of Engineering - Wiley, 2013, (2019) |
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Übergeordnetes Werk: |
year:2019 |
Links: |
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DOI / URN: |
10.1049/joe.2019.0685 |
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Katalog-ID: |
DOAJ074016148 |
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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 | |
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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 |
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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 |
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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 |
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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 |
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Sheng Hu |
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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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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 |
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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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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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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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BLUE filter with fused range estimation |
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BLUE filter with fused range estimation |
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blue filter with fused range estimation |
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BLUE filter with fused range estimation |
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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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BLUE filter with fused range estimation |
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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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