Parameter Estimation of an AUV Using the Maximum Likelihood method and a Kalman Filter with Fading Memory

The aim of this paper is to estimate the unknown hydrodynamic coefficients of an AUV. Discrete Kalman filter with fading memory is implemented to estimate the state variables of system and Maximum likelihood is used for the parameter estimation, and with a damped Newton method the cost function is optimized. Both process and measurement noises are assumed. To check the accuracy of the algorithm a model of SNUUV-I is used. The dynamic of AUV in the diving mode is identified. It is observed that the algorithm of parameter estimation is accurate and by using Kalman filter with fading memory and damped Newton method instead of the regular Kalman Filter, the accuracy of the results has improved.