Abstract
This paper presents an adaptive Kalman filtering approach that uses a variational Bayesian approximation method for robust navigation for Autonomous Underwater Vehicles. The integrated navigation system is composed of a strapdown inertial navigation system as main sensor along with Doppler Velocity Log (DVL), depthmeter and compass (all of them with different sampling rates) as complementary sensors. The proposed data integration multi-sensor and multi-rate adaptive algorithm considers unknown and time-varying statistical parameters of the measurement and process noises. The experimental tests show that the proposed algorithm is more accurate in terms of estimation of position, velocity and attitude, and it is more robust against outliers in the DVL measurements when compared with the extended Kalman filter and the error state Kalman filter approaches.
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