A Support Vector Regression-Based Integrated Navigation Method for Underwater Vehicles

When Doppler velocity log (DVL) works in a complex underwater environment, it has the possibility of malfunction at any time, which will affect the positioning accuracy of underwater integrated navigation system (INS). In this work, the INS/DVL integrated navigation system model is established to deal with DVL malfunctions, and the support vector regression (SVR) algorithm is used to establish the velocity regression prediction model of DVL. An optimized grid search-genetic algorithm is used to select the best parameters of SVR. Simulations are designed to compare the results of SVR prediction model and isolating DVL during DVL failure. The semi-physical experiment is carried out to verify the validity and applicability of DVL velocity prediction model. The experimental results show that the INS/DVL integrated navigation system with the proposed model based on SVR performs better than the original integrated navigation system during DVL malfunction.