A 6 DoF Navigation Algorithm for Autonomous Underwater Vehicles

The objective of this paper is to compare the performance of a new proposed measurement assisted partial re-sampling (MAPR) filter against the performance of the extended Kalman filter and the mixture Monte Carlo localizer within the context of a navigation algorithm for a dynamic 6 DoF system. In this paper, an autonomous underwater vehicle (AUV) is used as the dynamic system. The performances of the above three filters in resolving a navigation solution are assessed by giving the AUV a sequence of trajectories that highlight the sensitivities of the navigation algorithm to noise. This paper demonstrates that the MAPR filter is capable of computing an estimate that, like the EKF, takes into account the dynamics of the system, but like all particle filters is also capable of estimating non-Gaussian distributions.