Error Compensation for Dead Reckoning Based on SVM

• Published in: 水下无人系统学报 Vol. 32; no. 6; pp. 1009 - 1017
• Main Authors: Xin LI, Xiaoming WANG, Jianguo WU, Jiwei ZHAO, Jiacheng XIN, Kai CHEN, Bin ZHANG
• Format: Journal Article
• Language: Chinese
• Published: Science Press (China) 01.12.2024
• Subjects: autonomous undersea vehicle; dead reckoning; error compensation; support vector machine
• ISSN: 2096-3920
• DOI: 10.11993/j.issn.2096-3920.2024-0004

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. Th