A Multisource PNT Fusion Algorithm Based on a Variance Genetic Model
As one of the long-term challenges faced by the International Maritime Organization (IMO), the global navigation satellite system (GNSS) has become increasingly complicated with the rapid development of intelligent ships and autonomous navigation ships. GNSS vulnerability is an important factor affe...
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| Published in: | International journal of control, automation, and systems Vol. 20; no. 4; pp. 1294 - 1304 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Bucheon / Seoul
Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers
01.04.2022
Springer Nature B.V 제어·로봇·시스템학회 |
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| ISSN: | 1598-6446, 2005-4092 |
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| Abstract | As one of the long-term challenges faced by the International Maritime Organization (IMO), the global navigation satellite system (GNSS) has become increasingly complicated with the rapid development of intelligent ships and autonomous navigation ships. GNSS vulnerability is an important factor affecting navigation safety. Therefore, we propose a multisource position, navigation and time (PNT) data fusion algorithm based on the study of multisource shipborne PNT system model. This algorithm uses the variance genetic model to estimate the measurement variance of a PNT source at the subsequent time step to obtain an estimated value that is close to the real value, thus producing an optimal fusion factor for each PNT source and obtaining highly reliable and high-precision PNT fusion data. The simulation and measurement results show that the multisource PNT fusion algorithm based on the variance genetic model can provide superior reliability and precision when the PNT source is disturbed by abnormal interference. |
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| AbstractList | As one of the long-term challenges faced by the International Maritime Organization (IMO), the global navigation satellite system (GNSS) has become increasingly complicated with the rapid development of intelligent ships and autonomous navigation ships. GNSS vulnerability is an important factor affecting navigation safety. Therefore, we propose a multisource position, navigation and time (PNT) data fusion algorithm based on the study of multisource shipborne PNT system model. This algorithm uses the variance genetic model to estimate the measurement variance of a PNT source at the subsequent time step to obtain an estimated value that is close to the real value, thus producing an optimal fusion factor for each PNT source and obtaining highly reliable and high-precision PNT fusion data. The simulation and measurement results show that the multisource PNT fusion algorithm based on the variance genetic model can provide superior reliability and precision when the PNT source is disturbed by abnormal interference. As one of the long-term challenges faced by the International Maritime Organization (IMO), the global navigation satellite system (GNSS) has become increasingly complicated with the rapid development of intelligent ships and autonomous navigation ships. GNSS vulnerability is an important factor affecting navigation safety. Therefore, we propose a multisource position, navigation and time (PNT) data fusion algorithm based on the study of multisource shipborne PNT system model. This algorithm uses the variance genetic model to estimate the measurement variance of a PNT source at the subsequent time step to obtain an estimated value that is close to the real value, thus producing an optimal fusion factor for each PNT source and obtaining highly reliable and high-precision PNT fusion data. The simulation and measurement results show that the multisource PNT fusion algorithm based on the variance genetic model can provide superior reliability and precision when the PNT source is disturbed by abnormal interference. KCI Citation Count: 5 |
| Author | Zhu, Yue Cao, Jingyun Li, Jinyuan Jia, Jing Hu, Qing |
| Author_xml | – sequence: 1 givenname: Qing surname: Hu fullname: Hu, Qing organization: Dalian Maritime University – sequence: 2 givenname: Jing surname: Jia fullname: Jia, Jing organization: Dalian Maritime University – sequence: 3 givenname: Yue orcidid: 0000-0002-9878-4549 surname: Zhu fullname: Zhu, Yue email: zy36572@163.com organization: Dalian Maritime University – sequence: 4 givenname: Jingyun surname: Cao fullname: Cao, Jingyun organization: Dalian Maritime University – sequence: 5 givenname: Jinyuan surname: Li fullname: Li, Jinyuan organization: Dalian Maritime University |
| BackLink | https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002821388$$DAccess content in National Research Foundation of Korea (NRF) |
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| Cites_doi | 10.1007/s10291-019-0877-4 10.1109/CCDC.2017.7979474 10.1088/0957-0233/23/10/105101 10.1016/j.measurement.2020.108391 10.1109/JSYST.2013.2283753 10.1109/JSEN.2019.2955210 10.1109/ICMA.2019.8816392 10.1109/CYBER.2018.8688120 10.1155/2017/4180510 10.1109/ESTEL.2012.6400177 10.1109/ACCESS.2019.2911025 10.1109/JSEN.2011.2107896 10.1109/CCSSE.2016.7784352 10.1007/s12555-019-0287-y 10.1016/j.eswa.2013.02.002 10.1007/1-4020-4415-1 10.1007/s42835-020-00378-w 10.1186/s13638-020-01747-9 10.1109/ICRA.2012.6225061 10.1109/JPROC.2016.2528538 |
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| Keywords | multisource data fusion algorithm GNSS vulnerability variance genetic model PNT |
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| References | J. Li and Y. Wu, “Realization of GNSS/INS tightly coupled navigation and reliability verification in intelligent driving system,” Proc. of IEEE 8th Annual International Conference on CYBER Technology in Automation, July 2018. DOI: https://doi.org/10.1109/CYBER.2018.8688120 SánchezDMelinPCastilloOA grey wolf optimizer for modular granular neural networks for human recognitionComputational Intelligence and Neuroscience20172017812610.1155/2017/4180510August ZhangY XA fusion methodology to bridge GPS outages for INS/GPS integrated navigation systemIEEE Access20197612966130610.1109/ACCESS.2019.2911025April PereaDMorellAToledoJAcostaLGNSS integration in the localization system of an autonomous vehicle based on particle weightingIEEE Sensors Journal20192063314332310.1109/JSEN.2019.2955210November TangQ HDaiX ZZhangHA blind multibeamforming anti-jamming algorithm for GNSS antenna arraysNavigation Positioning and Timing202072132138February J. Erik, O. Gerard, and B. Stephen, “eLoran initial operational capability-providing resilient PNT to mariners,” 2014. C. L. Liang and G. S. Huang, “Spatial compressive array processing scheme against multiple narrowband interferences for GNSS,” Proc. of 2012 IEEE First AESS European Conference on Satellite Telecommunications, pp. 1–6, October 2012. DeepakBPriyankaAVijayDPrabirBA new source difference artificial neural network for enhanced positioning accuracyMeasurement Science and Technology2012231010510110511110.1088/0957-0233/23/10/105101October YanJXuX SZhangTLiuY TWuLDesign of marine-based miniature tightly integrated SINS/GNSS navigation systemJournal of Chinese Inertial Technology2013216775780December E. Engler, “Comments to guidelines for shipborne position, navigation and timing (PNT) data processing adopted by the IMO as MSC.1/Circ. 1575,” October 2017. SrujanaADeepakBHongWPrabirBVijayDA low-cost INS/GPS integration methodology based on random forest regressionExpert Systems with Applications201340114653465910.1016/j.eswa.2013.02.002September C. Zhang, C. Guo, and M. Z. Guo, “Information fusion based on artificial intelligence method for SINS/GPS integrated navigation of marine vessel,” Navigation Positioning and Timing, vol. 15, no. 59, February 2020. G. Hao, Y. Li, M. Zhao, H. Li, and Y. F. Dou, “Covariance intersection fusion particle filter for nonlinear systems,” Proc. of 29th Chinese Control And Decision Conference, July 2017. DOI: https://doi.org/10.1109/CCDC.2017.7979474 ChienY RDesign of GPS anti-jamming systems using adaptive notch filtersIEEE Systems Journal20159245146010.1109/JSYST.2013.2283753June G. Salvadori, C. D. Michele, N. T. Kottegoda, Extremes in Nature, 2007. Grejner-BrzezinskaD ATothC KMooreTRaquetJ FMillerM MKealyAMultisensor navigation systems: A remedy for GNSS vulnerabilities?Proceedings of the IEEE201610461339135310.1109/JPROC.2016.2528538June Z. Wu and W. Wang, “INS/magnetometer integrated positioning based on neural network for bridging long-time GPS outages,” GPS Solutions, vol. 23, no. 3, June 2019. B. Fu, J. Liu, and Q. Wang, “Multi-sensor integrated navigation system for ships based on adaptive Kalman filter,” Proc. of 2019 IEEE International Conference on Mechatronics and Automation, August 2019. DOI: https://doi.org/10.1109/ICMA.2019.8816392 S. Leutenegger and R. Y. Siegwart, “A low-cost and fail-safe Inertial Navigation System for airplanes,” Proc. of IEEE International Conference on Robotics and Automation, June 2012. DOI: https://doi.org/10.1109/ICRA.2012.6225061 WeintritAZalewskiPGuidelines for multi-system shipborne radionavigation receivers dealing with the harmonized provision of PNT dataProc. of International Conference on Transport Systems Telematics2017715216233September M. Aslinezhad, A. Malekijavan, and P. Abbasi, “ANN-assisted robust GPS/INS information fusion to bridge GPS outage,” EURASIP Journal on Wireless Communications and Networking, June 2020. DOI: https://doi.org/10.1186/s13638-020-01747-9 M. S. Schlosser and K. Kroschel, “Worst case analysis of decentralized Kalman filters under communication constraints,” European Signal Processing Conference, April 2015. ChuangXQingCZhengL GHaoWResearch on LFM interference suppression technology based on fractional Fourier transform in GNSS receiverNavigation Positioning and Timing2018555460May Y. H. Zhu, Q. L. Wei, Y. Liu, and W. X. Qian, “Research on marine inertial/PLBL/polarized-light integrated navigation algorithm,” Proc. of 2016 2nd International Conference on Control Science and Systems Engineering, December 2016. DOI: https://doi.org/10.1109/CCSSE.2016.7784352 FangT HKimYParkS GSeoKParkS HGPS and eLoran integrated navigation for marine applications using augmented measurement equation based on range domainInternational Journal of Control, Automation, and Systems20201892349235910.1007/s12555-019-0287-yMay N. A. Bitar and A. Gavrilov, “A new method for compensating the errors of integrated navigation systems using artificial neural networks,” Measurement, January 2021. DOI: https://doi.org/10.1016/j.measurement.2020.108391 GaoSZhongYLiWRandom weighting method for multisensor data fusionIEEE Sensors Journal20111191955196110.1109/JSEN.2011.2107896January Dec. IALA Recommendation R-129 On GNSS Vulnerability and Mitigation Measures Edition 3. 2012. CaronFDuflosEPomorskiDVanheeghePGPS/IMU data fusion using multisensor Kalman filtering: Introduction of contextual aspectsNavigation Positioning and Timing201772221230June 758_CR22 758_CR23 758_CR24 D Sánchez (758_CR27) 2017; 2017 758_CR25 758_CR26 758_CR29 D A Grejner-Brzezinska (758_CR2) 2016; 104 758_CR1 A Weintrit (758_CR3) 2017; 715 A Srujana (758_CR11) 2013; 40 T H Fang (758_CR9) 2020; 18 J Yan (758_CR12) 2013; 21 758_CR13 758_CR14 F Caron (758_CR15) 2017; 7 Y R Chien (758_CR4) 2015; 9 D Perea (758_CR18) 2019; 20 758_CR10 758_CR19 758_CR7 758_CR8 758_CR16 Q H Tang (758_CR6) 2020; 7 758_CR17 X Chuang (758_CR5) 2018; 5 S Gao (758_CR28) 2011; 11 Y X Zhang (758_CR21) 2019; 7 B Deepak (758_CR20) 2012; 23 |
| References_xml | – reference: S. Leutenegger and R. Y. Siegwart, “A low-cost and fail-safe Inertial Navigation System for airplanes,” Proc. of IEEE International Conference on Robotics and Automation, June 2012. DOI: https://doi.org/10.1109/ICRA.2012.6225061 – reference: M. S. Schlosser and K. Kroschel, “Worst case analysis of decentralized Kalman filters under communication constraints,” European Signal Processing Conference, April 2015. – reference: ChuangXQingCZhengL GHaoWResearch on LFM interference suppression technology based on fractional Fourier transform in GNSS receiverNavigation Positioning and Timing2018555460May – reference: Z. Wu and W. Wang, “INS/magnetometer integrated positioning based on neural network for bridging long-time GPS outages,” GPS Solutions, vol. 23, no. 3, June 2019. – reference: J. Erik, O. Gerard, and B. Stephen, “eLoran initial operational capability-providing resilient PNT to mariners,” 2014. – reference: G. Salvadori, C. D. Michele, N. T. Kottegoda, Extremes in Nature, 2007. – reference: C. L. Liang and G. S. Huang, “Spatial compressive array processing scheme against multiple narrowband interferences for GNSS,” Proc. of 2012 IEEE First AESS European Conference on Satellite Telecommunications, pp. 1–6, October 2012. – reference: YanJXuX SZhangTLiuY TWuLDesign of marine-based miniature tightly integrated SINS/GNSS navigation systemJournal of Chinese Inertial Technology2013216775780December – reference: N. A. Bitar and A. Gavrilov, “A new method for compensating the errors of integrated navigation systems using artificial neural networks,” Measurement, January 2021. DOI: https://doi.org/10.1016/j.measurement.2020.108391 – reference: ZhangY XA fusion methodology to bridge GPS outages for INS/GPS integrated navigation systemIEEE Access20197612966130610.1109/ACCESS.2019.2911025April – reference: GaoSZhongYLiWRandom weighting method for multisensor data fusionIEEE Sensors Journal20111191955196110.1109/JSEN.2011.2107896January – reference: SrujanaADeepakBHongWPrabirBVijayDA low-cost INS/GPS integration methodology based on random forest regressionExpert Systems with Applications201340114653465910.1016/j.eswa.2013.02.002September – reference: TangQ HDaiX ZZhangHA blind multibeamforming anti-jamming algorithm for GNSS antenna arraysNavigation Positioning and Timing202072132138February – reference: FangT HKimYParkS GSeoKParkS HGPS and eLoran integrated navigation for marine applications using augmented measurement equation based on range domainInternational Journal of Control, Automation, and Systems20201892349235910.1007/s12555-019-0287-yMay – reference: B. Fu, J. Liu, and Q. Wang, “Multi-sensor integrated navigation system for ships based on adaptive Kalman filter,” Proc. of 2019 IEEE International Conference on Mechatronics and Automation, August 2019. DOI: https://doi.org/10.1109/ICMA.2019.8816392 – reference: Grejner-BrzezinskaD ATothC KMooreTRaquetJ FMillerM MKealyAMultisensor navigation systems: A remedy for GNSS vulnerabilities?Proceedings of the IEEE201610461339135310.1109/JPROC.2016.2528538June – reference: PereaDMorellAToledoJAcostaLGNSS integration in the localization system of an autonomous vehicle based on particle weightingIEEE Sensors Journal20192063314332310.1109/JSEN.2019.2955210November – reference: G. Hao, Y. Li, M. Zhao, H. Li, and Y. F. Dou, “Covariance intersection fusion particle filter for nonlinear systems,” Proc. of 29th Chinese Control And Decision Conference, July 2017. DOI: https://doi.org/10.1109/CCDC.2017.7979474 – reference: DeepakBPriyankaAVijayDPrabirBA new source difference artificial neural network for enhanced positioning accuracyMeasurement Science and Technology2012231010510110511110.1088/0957-0233/23/10/105101October – reference: M. Aslinezhad, A. Malekijavan, and P. Abbasi, “ANN-assisted robust GPS/INS information fusion to bridge GPS outage,” EURASIP Journal on Wireless Communications and Networking, June 2020. DOI: https://doi.org/10.1186/s13638-020-01747-9 – reference: Dec. IALA Recommendation R-129 On GNSS Vulnerability and Mitigation Measures Edition 3. 2012. – reference: ChienY RDesign of GPS anti-jamming systems using adaptive notch filtersIEEE Systems Journal20159245146010.1109/JSYST.2013.2283753June – reference: Y. H. Zhu, Q. L. Wei, Y. Liu, and W. X. Qian, “Research on marine inertial/PLBL/polarized-light integrated navigation algorithm,” Proc. of 2016 2nd International Conference on Control Science and Systems Engineering, December 2016. DOI: https://doi.org/10.1109/CCSSE.2016.7784352 – reference: CaronFDuflosEPomorskiDVanheeghePGPS/IMU data fusion using multisensor Kalman filtering: Introduction of contextual aspectsNavigation Positioning and Timing201772221230June – reference: C. Zhang, C. Guo, and M. Z. Guo, “Information fusion based on artificial intelligence method for SINS/GPS integrated navigation of marine vessel,” Navigation Positioning and Timing, vol. 15, no. 59, February 2020. – reference: SánchezDMelinPCastilloOA grey wolf optimizer for modular granular neural networks for human recognitionComputational Intelligence and Neuroscience20172017812610.1155/2017/4180510August – reference: WeintritAZalewskiPGuidelines for multi-system shipborne radionavigation receivers dealing with the harmonized provision of PNT dataProc. of International Conference on Transport Systems Telematics2017715216233September – reference: E. Engler, “Comments to guidelines for shipborne position, navigation and timing (PNT) data processing adopted by the IMO as MSC.1/Circ. 1575,” October 2017. – reference: J. Li and Y. Wu, “Realization of GNSS/INS tightly coupled navigation and reliability verification in intelligent driving system,” Proc. of IEEE 8th Annual International Conference on CYBER Technology in Automation, July 2018. DOI: https://doi.org/10.1109/CYBER.2018.8688120 – ident: 758_CR25 doi: 10.1007/s10291-019-0877-4 – ident: 758_CR8 – ident: 758_CR17 doi: 10.1109/CCDC.2017.7979474 – volume: 23 start-page: 105101 issue: 10 year: 2012 ident: 758_CR20 publication-title: Measurement Science and Technology doi: 10.1088/0957-0233/23/10/105101 – volume: 21 start-page: 775 issue: 6 year: 2013 ident: 758_CR12 publication-title: Journal of Chinese Inertial Technology – volume: 7 start-page: 132 issue: 2 year: 2020 ident: 758_CR6 publication-title: Navigation Positioning and Timing – ident: 758_CR23 doi: 10.1016/j.measurement.2020.108391 – volume: 9 start-page: 451 issue: 2 year: 2015 ident: 758_CR4 publication-title: IEEE Systems Journal doi: 10.1109/JSYST.2013.2283753 – volume: 20 start-page: 3314 issue: 6 year: 2019 ident: 758_CR18 publication-title: IEEE Sensors Journal doi: 10.1109/JSEN.2019.2955210 – ident: 758_CR19 doi: 10.1109/ICMA.2019.8816392 – ident: 758_CR14 – volume: 715 start-page: 216 year: 2017 ident: 758_CR3 publication-title: Proc. of International Conference on Transport Systems Telematics – ident: 758_CR16 – ident: 758_CR22 doi: 10.1109/CYBER.2018.8688120 – ident: 758_CR1 – volume: 2017 start-page: 1 issue: 8 year: 2017 ident: 758_CR27 publication-title: Computational Intelligence and Neuroscience doi: 10.1155/2017/4180510 – ident: 758_CR7 doi: 10.1109/ESTEL.2012.6400177 – volume: 7 start-page: 61296 year: 2019 ident: 758_CR21 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2911025 – volume: 11 start-page: 1955 issue: 9 year: 2011 ident: 758_CR28 publication-title: IEEE Sensors Journal doi: 10.1109/JSEN.2011.2107896 – ident: 758_CR13 doi: 10.1109/CCSSE.2016.7784352 – volume: 18 start-page: 2349 issue: 9 year: 2020 ident: 758_CR9 publication-title: International Journal of Control, Automation, and Systems doi: 10.1007/s12555-019-0287-y – volume: 40 start-page: 4653 issue: 11 year: 2013 ident: 758_CR11 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2013.02.002 – volume: 7 start-page: 221 issue: 2 year: 2017 ident: 758_CR15 publication-title: Navigation Positioning and Timing – ident: 758_CR29 doi: 10.1007/1-4020-4415-1 – ident: 758_CR26 doi: 10.1007/s42835-020-00378-w – ident: 758_CR24 doi: 10.1186/s13638-020-01747-9 – ident: 758_CR10 doi: 10.1109/ICRA.2012.6225061 – volume: 104 start-page: 1339 issue: 6 year: 2016 ident: 758_CR2 publication-title: Proceedings of the IEEE doi: 10.1109/JPROC.2016.2528538 – volume: 5 start-page: 54 issue: 5 year: 2018 ident: 758_CR5 publication-title: Navigation Positioning and Timing |
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| SubjectTerms | Algorithms Autonomous navigation Control Data integration Engineering Global navigation satellite system Mechatronics Navigation safety Regular Papers Robotics Ships 제어계측공학 |
| Title | A Multisource PNT Fusion Algorithm Based on a Variance Genetic Model |
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