Error Compensation for Dead Reckoning Based on SVM
In the use of machine learning methods for error compensation in dead reckoning of an autonomous undersea vehicle(AUV), the neural network algorithm is commonly used. However, neural networks require a large number of training samples to achieve stable training results. To solve this problem, resear...
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Science Press (China)
2024-12-01
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| Series: | 水下无人系统学报 |
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| Online Access: | https://sxwrxtxb.xml-journal.net/cn/article/doi/10.11993/j.issn.2096-3920.2024-0004 |
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| author | Xin LI Xiaoming WANG Jianguo WU Jiwei ZHAO Jiacheng XIN Kai CHEN Bin ZHANG |
| author_facet | Xin LI Xiaoming WANG Jianguo WU Jiwei ZHAO Jiacheng XIN Kai CHEN Bin ZHANG |
| author_sort | Xin LI |
| collection | DOAJ |
| description | In the use of machine learning methods for error compensation in dead reckoning of an autonomous undersea vehicle(AUV), the neural network algorithm is commonly used. However, neural networks require a large number of training samples to achieve stable training results. To solve this problem, research was conducted on the application of support vector machine(SVM) for error compensation in dead reckoning. By utilizing SVM, an error compensation model was trained to correct the errors in dead reckoning, thereby improving navigational accuracy. The error compensation model takes seven parameters as input: pitch angle, roll angle, course angle, forward, right, and upward velocity of the Doppler velocity log(DVL) relative to the ground, and dead reckoning time of the AUV. The difference in latitude and longitude provided by the global positioning system(GPS) and inertial navigation system(INS) + DVL combination compared with latitude and longitude obtained from dead reckoning serves as the output of the model. The SVM trained model and the neural network trained model show a relative error of 0.28% and 0.93%, respectively, when the amount of data is limited. Through lake tests, it is concluded that the model trained by SVM can control the relative error of dead reckoning within 0.5%. |
| format | Article |
| id | doaj-art-26c8a3568ded4dc4b9b79ae681e4df97 |
| institution | Kabale University |
| issn | 2096-3920 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Science Press (China) |
| record_format | Article |
| series | 水下无人系统学报 |
| spelling | doaj-art-26c8a3568ded4dc4b9b79ae681e4df972025-01-07T02:42:15ZzhoScience Press (China)水下无人系统学报2096-39202024-12-013261009101710.11993/j.issn.2096-3920.2024-00042024-0004Error Compensation for Dead Reckoning Based on SVMXin LI0Xiaoming WANG1Jianguo WU2Jiwei ZHAO3Jiacheng XIN4Kai CHEN5Bin ZHANG6School of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300202, ChinaSchool of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300202, ChinaTianjin Hanhai Lanfan Marine Technology Co., Ltd, Tianjin 300300, ChinaTianjin Hanhai Lanfan Marine Technology Co., Ltd, Tianjin 300300, ChinaTianjin Hanhai Lanfan Marine Technology Co., Ltd, Tianjin 300300, ChinaTianjin Hanhai Lanfan Marine Technology Co., Ltd, Tianjin 300300, ChinaTianjin Hanhai Lanfan Marine Technology Co., Ltd, Tianjin 300300, ChinaIn the use of machine learning methods for error compensation in dead reckoning of an autonomous undersea vehicle(AUV), the neural network algorithm is commonly used. However, neural networks require a large number of training samples to achieve stable training results. To solve this problem, research was conducted on the application of support vector machine(SVM) for error compensation in dead reckoning. By utilizing SVM, an error compensation model was trained to correct the errors in dead reckoning, thereby improving navigational accuracy. The error compensation model takes seven parameters as input: pitch angle, roll angle, course angle, forward, right, and upward velocity of the Doppler velocity log(DVL) relative to the ground, and dead reckoning time of the AUV. The difference in latitude and longitude provided by the global positioning system(GPS) and inertial navigation system(INS) + DVL combination compared with latitude and longitude obtained from dead reckoning serves as the output of the model. The SVM trained model and the neural network trained model show a relative error of 0.28% and 0.93%, respectively, when the amount of data is limited. Through lake tests, it is concluded that the model trained by SVM can control the relative error of dead reckoning within 0.5%.https://sxwrxtxb.xml-journal.net/cn/article/doi/10.11993/j.issn.2096-3920.2024-0004autonomous undersea vehicledead reckoningsupport vector machineerror compensation |
| spellingShingle | Xin LI Xiaoming WANG Jianguo WU Jiwei ZHAO Jiacheng XIN Kai CHEN Bin ZHANG Error Compensation for Dead Reckoning Based on SVM 水下无人系统学报 autonomous undersea vehicle dead reckoning support vector machine error compensation |
| title | Error Compensation for Dead Reckoning Based on SVM |
| title_full | Error Compensation for Dead Reckoning Based on SVM |
| title_fullStr | Error Compensation for Dead Reckoning Based on SVM |
| title_full_unstemmed | Error Compensation for Dead Reckoning Based on SVM |
| title_short | Error Compensation for Dead Reckoning Based on SVM |
| title_sort | error compensation for dead reckoning based on svm |
| topic | autonomous undersea vehicle dead reckoning support vector machine error compensation |
| url | https://sxwrxtxb.xml-journal.net/cn/article/doi/10.11993/j.issn.2096-3920.2024-0004 |
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