A Novel Hybrid of a Fading Filter and an Extreme Learning Machine for GPS/INS during GPS Outages
In this paper, a novel algorithm based on the combination of a fading filter (FF) and an extreme learning machine (ELM) is presented for Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation systems. In order to increase the filtering accuracy of the model, a variable...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/18/11/3863 |
| _version_ | 1798040502540435456 |
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| author | Di Wang Xiaosu Xu Yongyun Zhu |
| author_facet | Di Wang Xiaosu Xu Yongyun Zhu |
| author_sort | Di Wang |
| collection | DOAJ |
| description | In this paper, a novel algorithm based on the combination of a fading filter (FF) and an extreme learning machine (ELM) is presented for Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation systems. In order to increase the filtering accuracy of the model, a variable fading factor fading filter based on the fading factor is proposed. It adjusts the fading factor by the ratio of the estimated covariance before and after the moment which proves to have excellent performance in our experiment. An extreme learning machine based on a Fourier orthogonal basis function is introduced that considers the deterioration of the accuracy of the navigation system during GPS outages and has a higher positioning accuracy and faster learning speed than the typical neural network learning algorithm. In the end, a simulation and real road test are performed to verify the effectiveness of this algorithm. The results show that the accuracy of the fading filter based on a variable fading factor is clearly improved, and the proposed improved ELM algorithm can provide position corrections during GPS outages more effectively than the other algorithms (ELM and the traditional radial basis function neural network). |
| first_indexed | 2024-04-11T22:08:30Z |
| format | Article |
| id | doaj.art-ecc9a83a5eec4d0688bc3a5d977179ef |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:08:30Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ecc9a83a5eec4d0688bc3a5d977179ef2022-12-22T04:00:38ZengMDPI AGSensors1424-82202018-11-011811386310.3390/s18113863s18113863A Novel Hybrid of a Fading Filter and an Extreme Learning Machine for GPS/INS during GPS OutagesDi Wang0Xiaosu Xu1Yongyun Zhu2Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Southeast University, Nanjing 210096, ChinaKey Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Southeast University, Nanjing 210096, ChinaKey Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Southeast University, Nanjing 210096, ChinaIn this paper, a novel algorithm based on the combination of a fading filter (FF) and an extreme learning machine (ELM) is presented for Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation systems. In order to increase the filtering accuracy of the model, a variable fading factor fading filter based on the fading factor is proposed. It adjusts the fading factor by the ratio of the estimated covariance before and after the moment which proves to have excellent performance in our experiment. An extreme learning machine based on a Fourier orthogonal basis function is introduced that considers the deterioration of the accuracy of the navigation system during GPS outages and has a higher positioning accuracy and faster learning speed than the typical neural network learning algorithm. In the end, a simulation and real road test are performed to verify the effectiveness of this algorithm. The results show that the accuracy of the fading filter based on a variable fading factor is clearly improved, and the proposed improved ELM algorithm can provide position corrections during GPS outages more effectively than the other algorithms (ELM and the traditional radial basis function neural network).https://www.mdpi.com/1424-8220/18/11/3863fading filterextreme learning machineGPS/INSintegrated navigation |
| spellingShingle | Di Wang Xiaosu Xu Yongyun Zhu A Novel Hybrid of a Fading Filter and an Extreme Learning Machine for GPS/INS during GPS Outages Sensors fading filter extreme learning machine GPS/INS integrated navigation |
| title | A Novel Hybrid of a Fading Filter and an Extreme Learning Machine for GPS/INS during GPS Outages |
| title_full | A Novel Hybrid of a Fading Filter and an Extreme Learning Machine for GPS/INS during GPS Outages |
| title_fullStr | A Novel Hybrid of a Fading Filter and an Extreme Learning Machine for GPS/INS during GPS Outages |
| title_full_unstemmed | A Novel Hybrid of a Fading Filter and an Extreme Learning Machine for GPS/INS during GPS Outages |
| title_short | A Novel Hybrid of a Fading Filter and an Extreme Learning Machine for GPS/INS during GPS Outages |
| title_sort | novel hybrid of a fading filter and an extreme learning machine for gps ins during gps outages |
| topic | fading filter extreme learning machine GPS/INS integrated navigation |
| url | https://www.mdpi.com/1424-8220/18/11/3863 |
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