Maximum Correntropy Polynomial Chaos Kalman Filter for Underwater Navigation

TL;DR

This paper introduces a maximum correntropy polynomial chaos Kalman filter for underwater navigation, effectively handling non-Gaussian noise and improving position and orientation estimation accuracy over traditional methods.

Contribution

It develops a novel filter that combines polynomial chaos with maximum correntropy criteria for robust underwater navigation under heavy-tailed noise conditions.

Findings

Enhanced estimation accuracy compared to existing filters

Effective handling of non-Gaussian, impulsive noise

Analysis of computational complexity of the proposed filter

Abstract

This paper develops an underwater navigation solution that utilizes a strapdown inertial navigation system (SINS) and fuses a set of auxiliary sensors such as an acoustic positioning system, Doppler velocity log, depth meter, attitude meter, and magnetometer to accurately estimate an underwater vessel's position and orientation. The conventional integrated navigation system assumes Gaussian measurement noise, while in reality, the noises are non-Gaussian, particularly contaminated by heavy-tailed impulsive noises. To address this issue, and to fuse the system model with the acquired sensor measurements efficiently, we develop a square root polynomial chaos Kalman filter based on maximum correntropy criteria. The filter is initialized using acoustic beaconing to accurately locate the initial position of the vehicle. The computational complexity of the proposed filter is calculated in terms of flops count. The proposed method is compared with the existing maximum correntropy sigma point filters in terms of estimation accuracy and computational complexity. The simulation results demonstrate an improved accuracy compared to the conventional deterministic sample point filters.