Model-Aided Strapdown Inertial Navigation Integrated Method for AUV Based on H8 Filtering

Abstract

The integration of Strapdown inertial navigation system (SINS) and Doppler Velocity Log (DVL) is uesd widely in autonomous underwater vehicle (AUV) navigation. However, when the bottom depth is beyond of the DVL detection region, the integrated mode is not effective, thus the error of SINS increases very fast. In this situation, the mathematical model of AUV is constructed based on its dynamics characters. Utilizing the position and velocity output from AUV model to correct SINS, the SINS precision is improved by integrated navigation. Aiming at the problem that traditional Kalman filtering needs to know accurate noise statistics and system model, H∞ filtering algorithm is used in this paper to restrain the precision and reliability of the system. Simulation results show that model-aided SINS integrated navigation method based on H∞ filtering can improve the precision of SINS, and effectively restrain the Kalman filtering divergence when the model is inaccurate.