Abstract
In this paper, a novel robust particle filter is proposed to address the measurement outliers occurring in the multiple autonomous underwater vehicles (AUVs) based cooperative navigation (CN). As compared with the classic unscented particle filter (UPF) based on Gaussian assumption of measurement noise, the proposed robust particle filter based on the maximum correntropy criterion (MCC) exhibits better robustness against heavy-tailed measurement noises that are often induced by measurement outliers in CN systems. Furthermore, the proposed robust particle filter is computationally much more efficient than existing robust UPF due to the use of a Kullback-Leibler distance-resampling to adjust the number of particles online. Experimental results based on actual lake trial show that the proposed maximum correntropy based unscented particle filter (MCUPF) has better estimation accuracy than existing state-of-the-art robust filters for CN systems with heavy-tailed measurement noises, and the proposed MCUPF has lower computational complexity than existing robust particle filters.
Highlights
Accurate navigation and localization of autonomous underwater vehicles (AUVs) are paramount for AUV autonomy
+ yk − yrk where xk and yk are east and north positions of AUVs at time k, respectively; xkr and yrk are east and north positions of communication and navigation aid (CNA) at time k provided periodically by the acoustic modern, respectively; ∆t is the sampling time, ŝk and ĉk are starboard and forward velocities at time k, respectively, which are provided by Doppler velocity log (DVL); θk is heading angle measured by compass
For making up the defect, the proposed maximum correntropy based unscented particle filter (MCUPF) replace the resampling process with Kullback-Leibler Divergence (KLD)-resampling, making up the defect, the proposed MCUPF replace the resampling process with KLD-resampling, which can adjust the number of particles over time to determine the minimum amount of particles which can adjust the number of particles over time to determine the minimum amount of particles required to guarantee the estimation quality and reduce computing effort
Summary
Accurate navigation and localization of autonomous underwater vehicles (AUVs) are paramount for AUV autonomy. By approximating the posterior density function (PDF) as a Student’s t distribution, a robust Student’s t based nonlinear filter (RSTNF) has been proposed [30,31,32,33] Such Student’s t approximation may be unreasonable in some engineering applications with slightly heavy-tailed measurement noises, which may result in deteriorating filtering performance [30]. To solve this problem, a novel robust Student’s t based Kalman filter (RSTKF) has been proposed based on variational-Bayesian (VB) method [26]. To further improve the positioning accuracy of UPF under non-Gaussian heavy-tailed measurement noise in CN of AUVs, a new maximum correntropy based unscented particle filter (MCUPF) is proposed, which modifies the update process of importance sampling of UPF.
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