A Novel ML-Aided Methodology for SINS/GPS Integrated Navigation Systems during GPS Outages
To improve the navigation accuracy for land vehicles during global positioning system (GPS) outages, a machine learning (ML) aided methodology to integrate a strap-down inertial navigation system (SINS) and GPS system is proposed, as follows. When a GPS signal is available, an online sequential extr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/23/5932 |
| _version_ | 1797462257416798208 |
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| author | Jin Sun Zhengyu Chen Fu Wang |
| author_facet | Jin Sun Zhengyu Chen Fu Wang |
| author_sort | Jin Sun |
| collection | DOAJ |
| description | To improve the navigation accuracy for land vehicles during global positioning system (GPS) outages, a machine learning (ML) aided methodology to integrate a strap-down inertial navigation system (SINS) and GPS system is proposed, as follows. When a GPS signal is available, an online sequential extreme learning machine with a dynamic forgetting factor (DOS-ELM) algorithm is used to train the mapping model between the SINS’ acceleration, specific force, speed/position increments outputs, and the GPS’ speed/position increments. When a GPS signal is unavailable, GPS speed/velocity measurements are replaced with prediction output of the well-trained DOS-ELM module’s prediction output, and information fusion with the SINS reduces the degree of system error divergence. A land vehicle field experiment’s actual sensor data were collected online, and the DOS-ELM-aided methodology for the SINS/GPS integrated navigation systems was applied. The simulation results indicate that the proposed methodology can reduce the degree of system error divergence and then obtain accurate and reliable navigation information during GPS outages. |
| first_indexed | 2024-03-09T17:33:56Z |
| format | Article |
| id | doaj.art-8e29562e94d248ff92a2645d646659d7 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T17:33:56Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8e29562e94d248ff92a2645d646659d72023-11-24T12:02:49ZengMDPI AGRemote Sensing2072-42922022-11-011423593210.3390/rs14235932A Novel ML-Aided Methodology for SINS/GPS Integrated Navigation Systems during GPS OutagesJin Sun0Zhengyu Chen1Fu Wang2School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaCollege of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaEast China Institute of Photo-Electron IC, Suzhou 215129, ChinaTo improve the navigation accuracy for land vehicles during global positioning system (GPS) outages, a machine learning (ML) aided methodology to integrate a strap-down inertial navigation system (SINS) and GPS system is proposed, as follows. When a GPS signal is available, an online sequential extreme learning machine with a dynamic forgetting factor (DOS-ELM) algorithm is used to train the mapping model between the SINS’ acceleration, specific force, speed/position increments outputs, and the GPS’ speed/position increments. When a GPS signal is unavailable, GPS speed/velocity measurements are replaced with prediction output of the well-trained DOS-ELM module’s prediction output, and information fusion with the SINS reduces the degree of system error divergence. A land vehicle field experiment’s actual sensor data were collected online, and the DOS-ELM-aided methodology for the SINS/GPS integrated navigation systems was applied. The simulation results indicate that the proposed methodology can reduce the degree of system error divergence and then obtain accurate and reliable navigation information during GPS outages.https://www.mdpi.com/2072-4292/14/23/5932global positioning system (GPS) outagesstrap-down inertial navigation system (SINS)machine learning (ML)online sequential extreme learning machine with dynamic forgetting factor (DOS-ELM)Kalman filtering (KF)SINS/GPS integrated systems |
| spellingShingle | Jin Sun Zhengyu Chen Fu Wang A Novel ML-Aided Methodology for SINS/GPS Integrated Navigation Systems during GPS Outages Remote Sensing global positioning system (GPS) outages strap-down inertial navigation system (SINS) machine learning (ML) online sequential extreme learning machine with dynamic forgetting factor (DOS-ELM) Kalman filtering (KF) SINS/GPS integrated systems |
| title | A Novel ML-Aided Methodology for SINS/GPS Integrated Navigation Systems during GPS Outages |
| title_full | A Novel ML-Aided Methodology for SINS/GPS Integrated Navigation Systems during GPS Outages |
| title_fullStr | A Novel ML-Aided Methodology for SINS/GPS Integrated Navigation Systems during GPS Outages |
| title_full_unstemmed | A Novel ML-Aided Methodology for SINS/GPS Integrated Navigation Systems during GPS Outages |
| title_short | A Novel ML-Aided Methodology for SINS/GPS Integrated Navigation Systems during GPS Outages |
| title_sort | novel ml aided methodology for sins gps integrated navigation systems during gps outages |
| topic | global positioning system (GPS) outages strap-down inertial navigation system (SINS) machine learning (ML) online sequential extreme learning machine with dynamic forgetting factor (DOS-ELM) Kalman filtering (KF) SINS/GPS integrated systems |
| url | https://www.mdpi.com/2072-4292/14/23/5932 |
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