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.

当多普勒计程仪（DVL）在复杂的水下环境中工作时，它随时都有可能发生故障，这将影响水下组合导航系统（INS）的定位精度。在这项工作中，建立了INS/DVL组合导航系统模型以应对DVL故障，并使用支持向量回归（SVR）算法建立了DVL的速度回归预测模型。采用一种优化的网格搜索 - 遗传算法来选择SVR的最佳参数。设计了仿真实验来比较DVL故障期间SVR预测模型和隔离DVL的结果。进行了半实物实验以验证DVL速度预测模型的有效性和适用性。实验结果表明，在DVL发生故障时，带有基于SVR所提出模型的INS/DVL组合导航系统比原始组合导航系统性能更好。