Tightly Integrated Doppler Velocity Log Aided Inertial Navigational System
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Background: Underwater navigation is an important aid for many industries, including oil and gas exploration, marine and subsea operations. These industries are highly dependent on a detailed map of the seabed, which can be supplied by AUVs, in order to conduct underwater operations safely and reliably. One commonly used velocity sensor on AUVs are called Doppler Velocity Log (DVL). Such sensors utilize the Doppler shift in sonic pulses to calculate vehicle velocities for system navigation.The main focus in this thesis has been on examining the quality of a tightly integrated DVL (referred to as Method 1) aided INS and to compare the quality of this method with an cartesian DVL (referred to as Method 2). Testing the performance of the two methods on real sensor data. Method:Analyses and simulations were carried out by the use of the generic aided inertial navigation software developed by Kongsberg Maritime, NavLab. NavLab is implemented in Matlab, and is used for performing navigation calculations for navigational purposes. Particular attention was given to the velocity measurements from the DVL device, as the velocity measurements bounds the velocity error of the navigation system. Findings:The proposed implementation of the tightly integrated DVL was proven to be a feasible method, as the Extended Kalman filter (EKF) was able to estimate velocities in transducer beams with an approximate mean errors of 0.02 %. However, the EKF was not tuned for Method 1, meaning that the internal Kalman filter dynamics for Method 1 presented in this thesis are not sufficiently accounted for, and lead to that the navigational error did not decrease when using Method 1, relative to Method 2. This leads to a lack of a firm conclusion between the two methods. However, the work presented in this thesis forms a solid foundation for further research within the field of velocity and position estimation for AUVs in Kongsberg Maritime and the important issue of Extended Kalman filter tuning in Method 1 has been illuminated.