A Comparison of EKF and PF to Localize an Autonomous Underwater Vehicle (AUV) using Inertial Navigation System

Using inertial navigation techniques, the position of an autonomous underwater vehicle (AUV) has been estimated in this research. The Extended Kalman Filter (EKF) and the Particle Filter (PF) are the two filters that have been employed to deduce the AUV's location. The EKF technique relies on linearizing a system's state space equation using the current values of the state parameters. The Particle Filter (PF) relies on the repetitive calculation of pertinent probability distributions through the utilisation of substantial sampling and intermittent stochastic measures. These two filters are implemented using a nonlinear kinetic model of a sample AUV, and the results of the position estimation of the two filters are compared. The findings indicate that, despite the simplifications made in the linearization process, the results obtained using the Extended Kalman Filter (EKF) are more accurate in representing the actual trajectory of the vehicle compared to the estimations obtained using the Particle Filter (PF).