{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/106250"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/106250","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Reduced order extended kalman filter incorporating dynamics of an autonomous underwater vehicle for motion prediction","abstract":"Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs) are used for a wide variety of missions such as exploration and scientific research. One key challenge for these autonomous vehicles is the creation of a reliable motion prediction. Kinematic Extended Kalman Filters (EKF) have been applied for AUV motion prediction in the context of Simultaneous Localization and Mapping (SLAM) [1]. It has been suggested that a dynamics based EKF would produce more accurate predictions as it considers forces acting on the AUV. Presented in this thesis is an motion prediction EKF for AUVs using a simplified dynamic model. First, the dynamic model is presented and then the simplification process is shown. The filter was implemented with a simulator vehicle in an open-source marine vehicle simulator called UUV Simulator and the results were tested against those obtained through dead reckoning. Results show good predictions, although there are improvements needed before the EKF could be used on manned operational system.","abstract_html":"Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs) are used for a wide variety of missions such as exploration and scientific research. One key challenge for these autonomous vehicles is the creation of a reliable motion prediction. Kinematic Extended Kalman Filters (EKF) have been applied for AUV motion prediction in the context of Simultaneous Localization and Mapping (SLAM) [1]. It has been suggested that a dynamics based EKF would produce more accurate predictions as it considers forces acting on the AUV. Presented in this thesis is an motion prediction EKF for AUVs using a simplified dynamic model. First, the dynamic model is presented and then the simplification process is shown. The filter was implemented with a simulator vehicle in an open-source marine vehicle simulator called UUV Simulator and the results were tested against those obtained through dead reckoning. Results show good predictions, although there are improvements needed before the EKF could be used on manned operational system.","abstract_has_math":false,"creators":["Hascaryo, Rodra Wikan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Norris, William R.","Tran, Huy T."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-03-02T21:58:26Z","date_published":"2020-03-02T21:58:26Z","updated_at":"2026-07-22T22:24:45Z","subjects":["Extended Kalman Filter","Vehicle Dynamics","Autonomous Underwater Vehicle","Unmanned Underwater Vehicle","Marine Vehicle Dynamics","Kalman Filter"],"languages":["en"],"rights":["Copyright 2019 Rodra Hascaryo"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/106250","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Norris, William R.","Tran, Huy T."]},{"key":"dc:creator","label":"Author","values":["Hascaryo, Rodra Wikan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-03-02T21:58:26Z","2019-12-09","2019-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Extended Kalman Filter","Vehicle Dynamics","Autonomous Underwater Vehicle","Unmanned Underwater Vehicle","Marine Vehicle Dynamics","Kalman Filter"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Rodra Hascaryo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/106250"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs) are used for a wide variety of missions such as exploration and scientific research. One key challenge for these autonomous vehicles is the creation of a reliable motion prediction. Kinematic Extended Kalman Filters (EKF) have been applied for AUV motion prediction in the context of Simultaneous Localization and Mapping (SLAM) [1]. It has been suggested that a dynamics based EKF would produce more accurate predictions as it considers forces acting on the AUV. Presented in this thesis is an motion prediction EKF for AUVs using a simplified dynamic model. First, the dynamic model is presented and then the simplification process is shown. The filter was implemented with a simulator vehicle in an open-source marine vehicle simulator called UUV Simulator and the results were tested against those obtained through dead reckoning. Results show good predictions, although there are improvements needed before the EKF could be used on manned operational system.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-02-28 without embargo terms","The student, Rodra Hascaryo, accepted the attached license on 2019-12-05 at 09:53.","The student, Rodra Hascaryo, submitted this Thesis for approval on 2019-12-05 at 09:59.","This Thesis was approved for publication on 2019-12-09 at 13:10.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14712 on 2020-02-28 at 17:15:47","Made available in DSpace on 2020-03-02T21:58:26Z (GMT). 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Kinematic Extended Kalman Filters (EKF) have been applied for AUV motion prediction in the context of Simultaneous Localization and Mapping (SLAM) [1]. It has been suggested that a dynamics based EKF would produce more accurate predictions as it considers forces acting on the AUV. Presented in this thesis is an motion prediction EKF for AUVs using a simplified dynamic model. First, the dynamic model is presented and then the simplification process is shown. The filter was implemented with a simulator vehicle in an open-source marine vehicle simulator called UUV Simulator and the results were tested against those obtained through dead reckoning. Results show good predictions, although there are improvements needed before the EKF could be used on manned operational system.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-02-28 without embargo terms","The student, Rodra Hascaryo, accepted the attached license on 2019-12-05 at 09:53.","The student, Rodra Hascaryo, submitted this Thesis for approval on 2019-12-05 at 09:59.","This Thesis was approved for publication on 2019-12-09 at 13:10.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14712 on 2020-02-28 at 17:15:47","Made available in DSpace on 2020-03-02T21:58:26Z (GMT). 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