High-speed train positioning based on a combination of Beidou navigation, inertial navigation and an electronic map

Abstract To date, the studies on the combination positioning of high-speed trains have made great progress, but the positioning accuracy of these methods is relatively low. Map-matching positioning can improve positioning accuracy, but information transmission is time-consuming. Few studies incorpor...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yang, Hui [verfasserIn] Dong, Shuai-Qiang [verfasserIn] Xie, Chun-Hua [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	electronic map multi-objective optimization differential evolution algorithm Beidou navigation system high-speed train positioning

Anmerkung:	© Science China Press 2023

Übergeordnetes Werk:	Enthalten in: Science China / Information sciences - Science China Press, 2010, 66(2023), 7 vom: 29. Juni
Übergeordnetes Werk:	volume:66 ; year:2023 ; number:7 ; day:29 ; month:06

Links:	Volltext

DOI / URN:	10.1007/s11432-022-3659-2

Katalog-ID:	SPR052133842

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520			\|a Abstract To date, the studies on the combination positioning of high-speed trains have made great progress, but the positioning accuracy of these methods is relatively low. Map-matching positioning can improve positioning accuracy, but information transmission is time-consuming. Few studies incorporate it into combination positioning. To solve the problem, this paper proposes a high-speed train positioning method based on combining a Beidou navigation system, an inertial navigation system, and an electronic map. First, the combination positioning problem is transformed into a multi-objective optimization problem, which weights the direction similarity and distance error to form a fitness function and converts the railway line and the maximum error range of each positioning system into constraints. Second, an improved differential evolution algorithm is proposed to solve this problem. By referencing the gray wolf algorithm, the differential evolution algorithm updates individuals by varying toward the direction of multiple optimal values. Then, a new combination positioning algorithm for high-speed trains is proposed. In the simulation, the increase in positioning speed and accuracy is analyzed and validated. Compared to the current algorithms, the proposed algorithm performs better. The proposed method has practical value for improving the reliability and safety of train operations.
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allfields	10.1007/s11432-022-3659-2 doi (DE-627)SPR052133842 (SPR)s11432-022-3659-2-e DE-627 ger DE-627 rakwb eng 004 VZ Yang, Hui verfasserin aut High-speed train positioning based on a combination of Beidou navigation, inertial navigation and an electronic map 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press 2023 Abstract To date, the studies on the combination positioning of high-speed trains have made great progress, but the positioning accuracy of these methods is relatively low. Map-matching positioning can improve positioning accuracy, but information transmission is time-consuming. Few studies incorporate it into combination positioning. To solve the problem, this paper proposes a high-speed train positioning method based on combining a Beidou navigation system, an inertial navigation system, and an electronic map. First, the combination positioning problem is transformed into a multi-objective optimization problem, which weights the direction similarity and distance error to form a fitness function and converts the railway line and the maximum error range of each positioning system into constraints. Second, an improved differential evolution algorithm is proposed to solve this problem. By referencing the gray wolf algorithm, the differential evolution algorithm updates individuals by varying toward the direction of multiple optimal values. Then, a new combination positioning algorithm for high-speed trains is proposed. In the simulation, the increase in positioning speed and accuracy is analyzed and validated. Compared to the current algorithms, the proposed algorithm performs better. The proposed method has practical value for improving the reliability and safety of train operations. electronic map (dpeaa)DE-He213 multi-objective optimization (dpeaa)DE-He213 differential evolution algorithm (dpeaa)DE-He213 Beidou navigation system (dpeaa)DE-He213 high-speed train positioning (dpeaa)DE-He213 Dong, Shuai-Qiang verfasserin aut Xie, Chun-Hua verfasserin aut Enthalten in Science China / Information sciences Science China Press, 2010 66(2023), 7 vom: 29. Juni Online-Ressource (DE-627)623184362 (DE-600)2546745-1 (DE-576)322138388 1869-1919 nnns volume:66 year:2023 number:7 day:29 month:06 https://dx.doi.org/10.1007/s11432-022-3659-2 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 66 2023 7 29 06
spelling	10.1007/s11432-022-3659-2 doi (DE-627)SPR052133842 (SPR)s11432-022-3659-2-e DE-627 ger DE-627 rakwb eng 004 VZ Yang, Hui verfasserin aut High-speed train positioning based on a combination of Beidou navigation, inertial navigation and an electronic map 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press 2023 Abstract To date, the studies on the combination positioning of high-speed trains have made great progress, but the positioning accuracy of these methods is relatively low. Map-matching positioning can improve positioning accuracy, but information transmission is time-consuming. Few studies incorporate it into combination positioning. To solve the problem, this paper proposes a high-speed train positioning method based on combining a Beidou navigation system, an inertial navigation system, and an electronic map. First, the combination positioning problem is transformed into a multi-objective optimization problem, which weights the direction similarity and distance error to form a fitness function and converts the railway line and the maximum error range of each positioning system into constraints. Second, an improved differential evolution algorithm is proposed to solve this problem. By referencing the gray wolf algorithm, the differential evolution algorithm updates individuals by varying toward the direction of multiple optimal values. Then, a new combination positioning algorithm for high-speed trains is proposed. In the simulation, the increase in positioning speed and accuracy is analyzed and validated. Compared to the current algorithms, the proposed algorithm performs better. The proposed method has practical value for improving the reliability and safety of train operations. electronic map (dpeaa)DE-He213 multi-objective optimization (dpeaa)DE-He213 differential evolution algorithm (dpeaa)DE-He213 Beidou navigation system (dpeaa)DE-He213 high-speed train positioning (dpeaa)DE-He213 Dong, Shuai-Qiang verfasserin aut Xie, Chun-Hua verfasserin aut Enthalten in Science China / Information sciences Science China Press, 2010 66(2023), 7 vom: 29. Juni Online-Ressource (DE-627)623184362 (DE-600)2546745-1 (DE-576)322138388 1869-1919 nnns volume:66 year:2023 number:7 day:29 month:06 https://dx.doi.org/10.1007/s11432-022-3659-2 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 66 2023 7 29 06
allfields_unstemmed	10.1007/s11432-022-3659-2 doi (DE-627)SPR052133842 (SPR)s11432-022-3659-2-e DE-627 ger DE-627 rakwb eng 004 VZ Yang, Hui verfasserin aut High-speed train positioning based on a combination of Beidou navigation, inertial navigation and an electronic map 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press 2023 Abstract To date, the studies on the combination positioning of high-speed trains have made great progress, but the positioning accuracy of these methods is relatively low. Map-matching positioning can improve positioning accuracy, but information transmission is time-consuming. Few studies incorporate it into combination positioning. To solve the problem, this paper proposes a high-speed train positioning method based on combining a Beidou navigation system, an inertial navigation system, and an electronic map. First, the combination positioning problem is transformed into a multi-objective optimization problem, which weights the direction similarity and distance error to form a fitness function and converts the railway line and the maximum error range of each positioning system into constraints. Second, an improved differential evolution algorithm is proposed to solve this problem. By referencing the gray wolf algorithm, the differential evolution algorithm updates individuals by varying toward the direction of multiple optimal values. Then, a new combination positioning algorithm for high-speed trains is proposed. In the simulation, the increase in positioning speed and accuracy is analyzed and validated. Compared to the current algorithms, the proposed algorithm performs better. The proposed method has practical value for improving the reliability and safety of train operations. electronic map (dpeaa)DE-He213 multi-objective optimization (dpeaa)DE-He213 differential evolution algorithm (dpeaa)DE-He213 Beidou navigation system (dpeaa)DE-He213 high-speed train positioning (dpeaa)DE-He213 Dong, Shuai-Qiang verfasserin aut Xie, Chun-Hua verfasserin aut Enthalten in Science China / Information sciences Science China Press, 2010 66(2023), 7 vom: 29. Juni Online-Ressource (DE-627)623184362 (DE-600)2546745-1 (DE-576)322138388 1869-1919 nnns volume:66 year:2023 number:7 day:29 month:06 https://dx.doi.org/10.1007/s11432-022-3659-2 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 66 2023 7 29 06
allfieldsGer	10.1007/s11432-022-3659-2 doi (DE-627)SPR052133842 (SPR)s11432-022-3659-2-e DE-627 ger DE-627 rakwb eng 004 VZ Yang, Hui verfasserin aut High-speed train positioning based on a combination of Beidou navigation, inertial navigation and an electronic map 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press 2023 Abstract To date, the studies on the combination positioning of high-speed trains have made great progress, but the positioning accuracy of these methods is relatively low. Map-matching positioning can improve positioning accuracy, but information transmission is time-consuming. Few studies incorporate it into combination positioning. To solve the problem, this paper proposes a high-speed train positioning method based on combining a Beidou navigation system, an inertial navigation system, and an electronic map. First, the combination positioning problem is transformed into a multi-objective optimization problem, which weights the direction similarity and distance error to form a fitness function and converts the railway line and the maximum error range of each positioning system into constraints. Second, an improved differential evolution algorithm is proposed to solve this problem. By referencing the gray wolf algorithm, the differential evolution algorithm updates individuals by varying toward the direction of multiple optimal values. Then, a new combination positioning algorithm for high-speed trains is proposed. In the simulation, the increase in positioning speed and accuracy is analyzed and validated. Compared to the current algorithms, the proposed algorithm performs better. The proposed method has practical value for improving the reliability and safety of train operations. electronic map (dpeaa)DE-He213 multi-objective optimization (dpeaa)DE-He213 differential evolution algorithm (dpeaa)DE-He213 Beidou navigation system (dpeaa)DE-He213 high-speed train positioning (dpeaa)DE-He213 Dong, Shuai-Qiang verfasserin aut Xie, Chun-Hua verfasserin aut Enthalten in Science China / Information sciences Science China Press, 2010 66(2023), 7 vom: 29. Juni Online-Ressource (DE-627)623184362 (DE-600)2546745-1 (DE-576)322138388 1869-1919 nnns volume:66 year:2023 number:7 day:29 month:06 https://dx.doi.org/10.1007/s11432-022-3659-2 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 66 2023 7 29 06
allfieldsSound	10.1007/s11432-022-3659-2 doi (DE-627)SPR052133842 (SPR)s11432-022-3659-2-e DE-627 ger DE-627 rakwb eng 004 VZ Yang, Hui verfasserin aut High-speed train positioning based on a combination of Beidou navigation, inertial navigation and an electronic map 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press 2023 Abstract To date, the studies on the combination positioning of high-speed trains have made great progress, but the positioning accuracy of these methods is relatively low. Map-matching positioning can improve positioning accuracy, but information transmission is time-consuming. Few studies incorporate it into combination positioning. To solve the problem, this paper proposes a high-speed train positioning method based on combining a Beidou navigation system, an inertial navigation system, and an electronic map. First, the combination positioning problem is transformed into a multi-objective optimization problem, which weights the direction similarity and distance error to form a fitness function and converts the railway line and the maximum error range of each positioning system into constraints. Second, an improved differential evolution algorithm is proposed to solve this problem. By referencing the gray wolf algorithm, the differential evolution algorithm updates individuals by varying toward the direction of multiple optimal values. Then, a new combination positioning algorithm for high-speed trains is proposed. In the simulation, the increase in positioning speed and accuracy is analyzed and validated. Compared to the current algorithms, the proposed algorithm performs better. The proposed method has practical value for improving the reliability and safety of train operations. electronic map (dpeaa)DE-He213 multi-objective optimization (dpeaa)DE-He213 differential evolution algorithm (dpeaa)DE-He213 Beidou navigation system (dpeaa)DE-He213 high-speed train positioning (dpeaa)DE-He213 Dong, Shuai-Qiang verfasserin aut Xie, Chun-Hua verfasserin aut Enthalten in Science China / Information sciences Science China Press, 2010 66(2023), 7 vom: 29. Juni Online-Ressource (DE-627)623184362 (DE-600)2546745-1 (DE-576)322138388 1869-1919 nnns volume:66 year:2023 number:7 day:29 month:06 https://dx.doi.org/10.1007/s11432-022-3659-2 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 66 2023 7 29 06
language	English
source	Enthalten in Science China / Information sciences 66(2023), 7 vom: 29. Juni volume:66 year:2023 number:7 day:29 month:06
sourceStr	Enthalten in Science China / Information sciences 66(2023), 7 vom: 29. Juni volume:66 year:2023 number:7 day:29 month:06
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	electronic map multi-objective optimization differential evolution algorithm Beidou navigation system high-speed train positioning
dewey-raw	004
isfreeaccess_bool	false
container_title	Science China / Information sciences
authorswithroles_txt_mv	Yang, Hui @@aut@@ Dong, Shuai-Qiang @@aut@@ Xie, Chun-Hua @@aut@@
publishDateDaySort_date	2023-06-29T00:00:00Z
hierarchy_top_id	623184362
dewey-sort	14
id	SPR052133842
language_de	englisch
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author	Yang, Hui
spellingShingle	Yang, Hui ddc 004 misc electronic map misc multi-objective optimization misc differential evolution algorithm misc Beidou navigation system misc high-speed train positioning High-speed train positioning based on a combination of Beidou navigation, inertial navigation and an electronic map
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topic_title	004 VZ High-speed train positioning based on a combination of Beidou navigation, inertial navigation and an electronic map electronic map (dpeaa)DE-He213 multi-objective optimization (dpeaa)DE-He213 differential evolution algorithm (dpeaa)DE-He213 Beidou navigation system (dpeaa)DE-He213 high-speed train positioning (dpeaa)DE-He213
topic	ddc 004 misc electronic map misc multi-objective optimization misc differential evolution algorithm misc Beidou navigation system misc high-speed train positioning
topic_unstemmed	ddc 004 misc electronic map misc multi-objective optimization misc differential evolution algorithm misc Beidou navigation system misc high-speed train positioning
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title	High-speed train positioning based on a combination of Beidou navigation, inertial navigation and an electronic map
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title_full	High-speed train positioning based on a combination of Beidou navigation, inertial navigation and an electronic map
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title_sort	high-speed train positioning based on a combination of beidou navigation, inertial navigation and an electronic map
title_auth	High-speed train positioning based on a combination of Beidou navigation, inertial navigation and an electronic map
abstract	Abstract To date, the studies on the combination positioning of high-speed trains have made great progress, but the positioning accuracy of these methods is relatively low. Map-matching positioning can improve positioning accuracy, but information transmission is time-consuming. Few studies incorporate it into combination positioning. To solve the problem, this paper proposes a high-speed train positioning method based on combining a Beidou navigation system, an inertial navigation system, and an electronic map. First, the combination positioning problem is transformed into a multi-objective optimization problem, which weights the direction similarity and distance error to form a fitness function and converts the railway line and the maximum error range of each positioning system into constraints. Second, an improved differential evolution algorithm is proposed to solve this problem. By referencing the gray wolf algorithm, the differential evolution algorithm updates individuals by varying toward the direction of multiple optimal values. Then, a new combination positioning algorithm for high-speed trains is proposed. In the simulation, the increase in positioning speed and accuracy is analyzed and validated. Compared to the current algorithms, the proposed algorithm performs better. The proposed method has practical value for improving the reliability and safety of train operations. © Science China Press 2023
abstractGer	Abstract To date, the studies on the combination positioning of high-speed trains have made great progress, but the positioning accuracy of these methods is relatively low. Map-matching positioning can improve positioning accuracy, but information transmission is time-consuming. Few studies incorporate it into combination positioning. To solve the problem, this paper proposes a high-speed train positioning method based on combining a Beidou navigation system, an inertial navigation system, and an electronic map. First, the combination positioning problem is transformed into a multi-objective optimization problem, which weights the direction similarity and distance error to form a fitness function and converts the railway line and the maximum error range of each positioning system into constraints. Second, an improved differential evolution algorithm is proposed to solve this problem. By referencing the gray wolf algorithm, the differential evolution algorithm updates individuals by varying toward the direction of multiple optimal values. Then, a new combination positioning algorithm for high-speed trains is proposed. In the simulation, the increase in positioning speed and accuracy is analyzed and validated. Compared to the current algorithms, the proposed algorithm performs better. The proposed method has practical value for improving the reliability and safety of train operations. © Science China Press 2023
abstract_unstemmed	Abstract To date, the studies on the combination positioning of high-speed trains have made great progress, but the positioning accuracy of these methods is relatively low. Map-matching positioning can improve positioning accuracy, but information transmission is time-consuming. Few studies incorporate it into combination positioning. To solve the problem, this paper proposes a high-speed train positioning method based on combining a Beidou navigation system, an inertial navigation system, and an electronic map. First, the combination positioning problem is transformed into a multi-objective optimization problem, which weights the direction similarity and distance error to form a fitness function and converts the railway line and the maximum error range of each positioning system into constraints. Second, an improved differential evolution algorithm is proposed to solve this problem. By referencing the gray wolf algorithm, the differential evolution algorithm updates individuals by varying toward the direction of multiple optimal values. Then, a new combination positioning algorithm for high-speed trains is proposed. In the simulation, the increase in positioning speed and accuracy is analyzed and validated. Compared to the current algorithms, the proposed algorithm performs better. The proposed method has practical value for improving the reliability and safety of train operations. © Science China Press 2023
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container_issue	7
title_short	High-speed train positioning based on a combination of Beidou navigation, inertial navigation and an electronic map
url	https://dx.doi.org/10.1007/s11432-022-3659-2
remote_bool	true
author2	Dong, Shuai-Qiang Xie, Chun-Hua
author2Str	Dong, Shuai-Qiang Xie, Chun-Hua
ppnlink	623184362
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s11432-022-3659-2
up_date	2024-08-29T04:50:12.013Z
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High-speed train positioning based on a combination of Beidou navigation, inertial navigation and an electronic map

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