Model-Aided Localization and Navigation for Underwater Gliders Using Single-Beacon Travel-Time Differences.

Feng Hu,Jin Wang,Aiqun Zhang,Yeteng Luo,Jiancheng Yu,Wenming Jin,Xu Wang,Jie Sun

doi:10.3390/s20030893

Abstract

An accurate motion model and reliable measurements are required for autonomous underwater vehicle localization and navigation in underwater environments. However, without a propeller, underwater gliders have limited maneuverability and carrying capacity, which brings difficulties for modeling and measuring. In this paper, an extended Kalman filter (EKF)-based method, combining a modified kinematic model of underwater gliders with the travel-time differences between signals received from a single beacon, is proposed for estimating the glider positions in a predict-update cycle. First, to accurately establish a motion model for underwater gliders moving in the ocean, we introduce two modification parameters, the attack and drift angles, into a kinematic model of underwater gliders, along with depth-averaged current velocities. The attack and drift angles are calculated based on the coefficients of hydrodynamic forces and the sensor-measured angle variation over time. Then, instead of satisfying synchronization requirements, the travel-time differences between signals received from a single beacon, multiplied by the sound speed, are taken as the measurements. To further reduce the EKF estimation error, the Rauch-Tung-Striebel (RTS) smoothing method is merged into the EKF system. The proposed method is tested in a virtual spatiotemporal environment from an ocean model. The experimental results show that the performance of the RTS-EKF estimate is improved when compared with the motion model estimate, especially by 46% at the inflection point, at least in the particular study developed in this article.

Highlights

Underwater gliders are a type of autonomous underwater vehicles (AUVs) but without a propeller [1], which are great autonomous platforms fitted in a persistent underwater surveillance system [2,3]
The purpose of this process is to make the glider know its position and to better detect the target [6]. The limitation of this extended Kalman filter (EKF) system is that the state estimation error introduced by the inaccurate depth-averaged current velocity, which is estimated from the previous gliding cycle, cannot be fully corrected and the cumulative error will increase over time
Under the same case of Nv, the percentage for root-mean-square error (RMSE) ≤ 100 m corresponding to the RTS-EKF estimate is always larger than that of the motion model estimate, while the percentage for RMSE ≥ 500 m corresponding to the RTS-EKF estimate is always not greater than that of the motion model estimate

Summary

Introduction

Underwater gliders are a type of autonomous underwater vehicles (AUVs) but without a propeller [1], which are great autonomous platforms fitted in a persistent underwater surveillance system [2,3]. The prediction process of state estimation is established mainly based on motion models of AUVs. Based on the installed sensors, AUVs usually adopt inertial navigation by using the IMU-detected acceleration and the DVL-measured relative velocity of the AUV [13]. We combine a kinematic model of underwater gliders with the estimated attack and drift angles and add the depth-averaged current velocities [24]. The range variation from underwater gliders to a static acoustic beacon, calculated from the travel-time difference of two adjacent signals from the beacon, is set as the measurement for the EKF-based localization and navigation system.

Problem Statement

Modified Kinematic Model for the Sea-Wing Underwater Glider

Kinematic Model

Solution of Attack Angle

Solution of Drift Angle

Comparison with Dead Reckoning

EKF-Based Localization and Navigation System Modeling

System State Prediction

Measurement Model

Recursive Estimation of the System State

Estimation Improvement by RTS Smoothing

Simulation Based on Experimental and Model Data