Parameter estimation of AUV hydrodynamic coefficients using an augmented CKF

The parameter estimation of an underwater vehicle is a difficult task that can be approached in several ways. Popular methods include reduced-scale testing and CFD analysis. Both of these approaches require high initial costs and expert domain knowledge. The aim of this thesis is to estimate the underdetermined parameters of the Flatfish vehicle developed by Saipem by using an augmented CKF. The motivation for this approach is that these parameters can be estimated using previous mission data. This can drastically reduce costs when compared to scaled-down testing approaches. The objective of the thesis is to outline the modelling convention of the vehicle, the known parameters, and state what is to be estimated. The capability of the CKF will then be assessed in the supervised case using a simulation to determine the convergence capabilities of the CKF using a variety of signals. Then, using mission data, the parameters will be estimated in the model.