A Sensor-based Long Baseline Position and Velocity Navigation Filter for Underwater Vehicles

TL;DR

This paper introduces a novel sensor-based Long Baseline (LBL) navigation filter for underwater vehicles that directly incorporates range measurements, avoids inversion algorithms, and maintains global stability, matching EKF performance.

Contribution

The paper presents a fully nonlinear, sensor-based LBL navigation filter that explicitly embeds range measurements without linearization or inversion, ensuring global stability and improved performance.

Findings

The filter achieves global asymptotic stability.

It performs comparably to the Extended Kalman Filter.

It outperforms linear algebraic estimation filters.

Abstract

This paper presents a novel Long Baseline (LBL) position and velocity navigation filter for underwater vehicles based directly on the sensor measurements. The solution departs from previous approaches as the range measurements are explicitly embedded in the filter design, therefore avoiding inversion algorithms. Moreover, the nonlinear system dynamics are considered to their full extent and no linearizations are carried out whatsoever. The filter error dynamics are globally asymptotically stable (GAS) and it is shown, under simulation environment, that the filter achieves similar performance to the Extended Kalman Filter (EKF) and outperforms linear position and velocity filters based on algebraic estimates of the position obtained from the range measurements.