MEMS IMU navigation with model based dead-reckoning and one-way-travel-time acoustic range measurements for autonomous underwater vehicles
type: CreativeWork
description: Recent advances in acoustic navigation methodologies are enabling the way for AUVs to extend their submerged mission time and maintain a bounded XY position error. Additionally, advances in inertial sensor technology have drastically lowered the size, power consumption, and cost of these sensors. Nonetheless, these sensors are still noisy and accrue error over time. This thesis builds on the research and recent developments in single beacon one-waytravel- time (OWTT) acoustic navigation and investigates the degree of bounding position error for small AUVs with a minimal navigation strap-down sensor suite, relying mostly on a consumer grade microelectromechanical system (MEMS) inertial measurement unit (IMU) and a vehicle’s dynamic model velocity. An implementation of an Extended Kalman Filter (EKF) that includes IMU bias estimation and coupled with a range filter, is obtained in the field on two OceanServer Technology, Inc. Iver2 AUVs and one Bluefin Robotics SandShark 𝜇AUV. Results from these field trials on Ashumet Pond of Falmouth, Massachusetts, the Charles River of Cambridge, Massachusetts, and Monterey Bay near Santa Cruz, California show a navigation solution accuracy comparable to current standard navigation techniques.
abstract: Recent advances in acoustic navigation methodologies are enabling the way for AUVs to extend their submerged mission time and maintain a bounded XY position error. Additionally, advances in inertial sensor technology have drastically lowered the size, power consumption, and cost of these sensors. Nonetheless, these sensors are still noisy and accrue error over time. This thesis builds on the research and recent developments in single beacon one-waytravel- time (OWTT) acoustic navigation and investigates the degree of bounding position error for small AUVs with a minimal navigation strap-down sensor suite, relying mostly on a consumer grade microelectromechanical system (MEMS) inertial measurement unit (IMU) and a vehicle’s dynamic model velocity. An implementation of an Extended Kalman Filter (EKF) that includes IMU bias estimation and coupled with a range filter, is obtained in the field on two OceanServer Technology, Inc. Iver2 AUVs and one Bluefin Robotics SandShark 𝜇AUV. Results from these field trials on Ashumet Pond of Falmouth, Massachusetts, the Charles River of Cambridge, Massachusetts, and Monterey Bay near Santa Cruz, California show a navigation solution accuracy comparable to current standard navigation techniques.
author: Kepper, James
language: en_US
date published: 2017-09
date modified: 2026-07-27T22:05:02Z
provider: Woods Hole Oceanographic Institute
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Massachusetts Institute of Technology and Woods Hole Oceanographic Institution

PropertyValue
property ID: Handle
value: https://hdl.handle.net/1912/9198

PropertyValue
property ID: DOI
value: 10.1575/1912/9198