Ensemble and Fuzzy Kalman Filter for position estimation of an autonomous underwater vehicle based on dynamical system of AUV motion

- Ensemble Kalman Filter (EnKF) algorithm can be applied to estimate AUV position.
- Fuzzy Kalman Filter (FKF) algorithm can be applied to estimate AUV position.
- The system dynamic of AUV motion is used for true trajectory of estimation.
- EnKF estimation is better than FKF estimation in AUV position estimation.
- Performance of each method based on RMSE and computational time.

An underwater vehicle is useful in the monitoring of the unstructured and dangerous underwater conditions. One of the unmanned underwater vehicle is AUV. AUV is a robotic device that is driven through the water by a propulsion system, controlled and piloted by an onboard computer, and maneuverable in three dimensions. This research explains about position estimation of AUV based on the Ensemble Kalman Filter (EnKF) and the Fuzzy Kalman Filter (FKF). EnKF is used as the estimation method of AUV's position that maneuvering in 6 DOF (Degrees of Freedom) with the specified trajectory. The estimation results are simulated with Matlab. The simulations show the AUV position estimation based on the EnKF with some of the different ensembles and the comparison results of the position estimation between the EnKF and the FKF. The final result of these study shows that Ensemble Kalman Filter is better to estimate the trajectory of the dynamical equation of AUV motion with the error estimation of EnKF is 92% smaller in the x-position dan y-position, 6.5% smaller in the z-position, 93% smaller in the angle dan the computation of time is 50% faster than the estimation results of FKF.