Abstract
The navigation and localization of Autonomous Underwater Vehicle (AUV) face many challenges. In surface missions, AUV typically relies only on Global Positioning System (GPS) to perform navigation. However, the GPS data output frequency is low, usually only 1 Hz. Meanwhile, the navigation system is susceptible to interference from GPS measurement outliers. For underwater missions, without the assistance of additional underwater absolute positioning measurements, the unknown errors introduced by the navigation system modeling process and state estimation process will affect the navigation accuracy. Therefore, in this paper, we propose an integrated navigation method for small-sized AUV in shallow-sea applications. Firstly, a robust GPS/Attitude and Heading Reference System (AHRS)/Doppler Velocity Log (DVL) navigation method for surface missions is proposed to maintain high-frequency output and robustness. Secondly, a Multiple Model (MM) integrated navigation method is proposed for AUV underwater missions. An AHRS/DVL/NavNet sub-model is proposed to perform measurement correction to correct the unknown errors. At the same time, an adaptive AHRS/DVL sub-model based on Variational Bayesian (VB) is presented to estimate the time-varying measurement noise covariance to improve the state estimation accuracy without the extra external observations. Two sub-models run parallel based on the Interacting Multiple Model (IMM). The effectiveness of the proposed integrated navigation method is verified by actual sea trial data from Sailfish 210 AUV.
Published Version
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