A Model-Free Calibration Method of Inertial Navigation System and Doppler Sensors

Abstract

The calibration of inertial navigation system (INS) and Doppler Velocity Log (DVL) is an essential task through which the positioning accuracy of the INS/DVL system can be improved significantly. Most existing calibration methods are dependent on the pre-established DVL error model and the hypothesis of small misalignment angles between DVL and INS. An integral calibration method without pre-established model is proposed, which utilizes Genetic Algorithm (GA) and Support Vector Regression (SVR) to construct regression predictor for integral calibration and consolidated compensation. GPS and pressure sensors are used to collect training data to construct the training target output. During underwater navigation, the output of GA-SVR predictor is regarded as the calibrated velocity to construct INS/DVL system. Simulation and semi-physical experiment results show that compared with other traditional calibration algorithms, the calibration accuracy of proposed algorithm is greatly improved. In addition, the limitations of other traditional algorithms can be overcome, which means proposed algorithm can perform smoothly when the misalignment angles are large.