Unmanned Underwater Vehicles (UUVs) have become an essential tool for different underwater tasks. Compared with other unmanned systems, the navigation and localization for UUVs are particularly challenging due to the unavailability of Global Positioning System (GPS) signals underwater and the complexity of the unstable environment. Alternative methods such as acoustic positioning systems, Inertial Navigation Systems (INS), and the geophysical navigation approach are used for UUV navigation. Acoustic positioning systems utilize the characteristics of acoustic signals that have a lower absorption rate and a more extended propagation distance than electromagnetic signals underwater. The significant disadvantage of the INS is the “drift,” the unbounded error growth over time in the outputs. This thesis is aimed to study and test a combined UUV navigation system that fuses measurements from the INS, Doppler Velocity Log (DVL), and Short Baseline (SBL) acoustic positioning system to reduce the drift. Two Kalman filters are used to do the fusion: the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). After conducting the experiments and simulation, the results illustrated the INS/SBL fusion navigation approach was able to reduce the drift problems in the INS. Moreover, UKF showed a better performance than the EKF in the INS.
","abstract_html":"<p>Unmanned Underwater Vehicles (UUVs) have become an essential tool for different underwater tasks. Compared with other unmanned systems, the navigation and localization for UUVs are particularly challenging due to the unavailability of Global Positioning System (GPS) signals underwater and the complexity of the unstable environment. Alternative methods such as acoustic positioning systems, Inertial Navigation Systems (INS), and the geophysical navigation approach are used for UUV navigation. Acoustic positioning systems utilize the characteristics of acoustic signals that have a lower absorption rate and a more extended propagation distance than electromagnetic signals underwater. The significant disadvantage of the INS is the “drift,” the unbounded error growth over time in the outputs. This thesis is aimed to study and test a combined UUV navigation system that fuses measurements from the INS, Doppler Velocity Log (DVL), and Short Baseline (SBL) acoustic positioning system to reduce the drift. Two Kalman filters are used to do the fusion: the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). After conducting the experiments and simulation, the results illustrated the INS/SBL fusion navigation approach was able to reduce the drift problems in the INS. Moreover, UKF showed a better performance than the EKF in the INS.</p>","abstract_has_math":false,"creators":["Alzahrani, Khalid M"],"institution":null,"degree_name":"Master of Science in Mechanical Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-05-01T07:00:00Z","date_published":"2018-05-01T07:00:00Z","updated_at":"2026-07-27T19:25:37Z","subjects":["underwater vehicles","navigation","sensors","Mechanical Engineering","Navigation, Guidance, Control and Dynamics","Ocean Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/397","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Alzahrani, Khalid M"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Mechanical Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["underwater vehicles","navigation","sensors","Mechanical Engineering","Navigation, Guidance, Control and Dynamics","Ocean Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/397"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Unmanned Underwater Vehicles (UUVs) have become an essential tool for different underwater tasks. Compared with other unmanned systems, the navigation and localization for UUVs are particularly challenging due to the unavailability of Global Positioning System (GPS) signals underwater and the complexity of the unstable environment. Alternative methods such as acoustic positioning systems, Inertial Navigation Systems (INS), and the geophysical navigation approach are used for UUV navigation. Acoustic positioning systems utilize the characteristics of acoustic signals that have a lower absorption rate and a more extended propagation distance than electromagnetic signals underwater. The significant disadvantage of the INS is the “drift,” the unbounded error growth over time in the outputs. This thesis is aimed to study and test a combined UUV navigation system that fuses measurements from the INS, Doppler Velocity Log (DVL), and Short Baseline (SBL) acoustic positioning system to reduce the drift. Two Kalman filters are used to do the fusion: the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). After conducting the experiments and simulation, the results illustrated the INS/SBL fusion navigation approach was able to reduce the drift problems in the INS. Moreover, UKF showed a better performance than the EKF in the INS.
"]},{"key":"dc:title","label":"Title","values":["An Underwater Vehicle Navigation System Using Acoustic and Inertial Sensors"]}]}],"canonical_facts":{"dc:creator":["Alzahrani, Khalid M"],"dc:description.abstract":["Unmanned Underwater Vehicles (UUVs) have become an essential tool for different underwater tasks. Compared with other unmanned systems, the navigation and localization for UUVs are particularly challenging due to the unavailability of Global Positioning System (GPS) signals underwater and the complexity of the unstable environment. Alternative methods such as acoustic positioning systems, Inertial Navigation Systems (INS), and the geophysical navigation approach are used for UUV navigation. Acoustic positioning systems utilize the characteristics of acoustic signals that have a lower absorption rate and a more extended propagation distance than electromagnetic signals underwater. The significant disadvantage of the INS is the “drift,” the unbounded error growth over time in the outputs. This thesis is aimed to study and test a combined UUV navigation system that fuses measurements from the INS, Doppler Velocity Log (DVL), and Short Baseline (SBL) acoustic positioning system to reduce the drift. Two Kalman filters are used to do the fusion: the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). After conducting the experiments and simulation, the results illustrated the INS/SBL fusion navigation approach was able to reduce the drift problems in the INS. Moreover, UKF showed a better performance than the EKF in the INS.
"],"dc:identifier":["https://commons.erau.edu/edt/397"],"dc:subject":["underwater vehicles","navigation","sensors","Mechanical Engineering","Navigation, Guidance, Control and Dynamics","Ocean Engineering"],"dc:title":["An Underwater Vehicle Navigation System Using Acoustic and Inertial Sensors"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Mechanical Engineering"]},"updated_at":"2026-07-27T19:25:37Z"}