Priori-sensitive resampling particle filter for dynamic state estimation of UUVs

Abstract

The aim of this paper is to introduce priori sensitive resampling (PSR) based particle filter (PF) for prospective use in dynamic state estimation towards navigation of unmanned underwater vehicles (UUVs). The proposed method targets a common pitfall of conventional resampling based PFs, in the sense that classical resampling is likelihood biased operation which progressively leads to particle impoverishment and ultimately degrades the estimation quality. The presented method however generates a resampled population balanced between the significant regions of both likelihood and state transition prior. The algorithm is tested with a simulated navigation scenario for UUVs using simplified motion model. Results reveal that by using only a small population size, PSR provides a lower root mean square error (RMSE) of estimation in comparison to that obtained with Extended Kalman Filter (EKF) and classical resampling particle filter as well as an Exquisite Resampling algorithm. The method is also shown to be insensitive to significant simulated measurement outliers.