{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101218"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101218","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Target search methods for space situational awareness","abstract":"This work studies methods to detect target in an orbit around the Earth using a space based sensor. Searching for a target among a large set of candidate orbits is a difficult and time consuming problem. Considering orbital dynamics, sensor uncertainties and the initial size of candidate location distribution, it is desirable to develop efficient search techniques. In this work, information-theoretic methods for searching a target in a large probability distribution using a space based sensor is considered. One intuitive approach is to steer the sensor towards regions of high probability density. Alternatively, information-theoretic methods steer the sensor based on metrics of the information gain in the posterior probability distribution. Through simulation, it is shown that information-theoretic search methods produce greater knowledge about probability distribution of the target's orbit. We also present methods to lower the computing expense imposed on the computer on-board a space based sensor. The issue is addressed using data clustering technique called K-means clustering. It is shown that errors resulting from searching the target after clustering is much lower compared to errors resulting from searching targets at the locations of higher probability.","abstract_html":"This work studies methods to detect target in an orbit around the Earth using a space based sensor. Searching for a target among a large set of candidate orbits is a difficult and time consuming problem. Considering orbital dynamics, sensor uncertainties and the initial size of candidate location distribution, it is desirable to develop efficient search techniques. In this work, information-theoretic methods for searching a target in a large probability distribution using a space based sensor is considered. One intuitive approach is to steer the sensor towards regions of high probability density. Alternatively, information-theoretic methods steer the sensor based on metrics of the information gain in the posterior probability distribution. Through simulation, it is shown that information-theoretic search methods produce greater knowledge about probability distribution of the target's orbit. We also present methods to lower the computing expense imposed on the computer on-board a space based sensor. The issue is addressed using data clustering technique called K-means clustering. It is shown that errors resulting from searching the target after clustering is much lower compared to errors resulting from searching targets at the locations of higher probability.","abstract_has_math":false,"creators":["Patel, Mihir Jagdishbhai"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Ho, Koki"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:36:53Z","date_published":"2018-09-04T20:36:53Z","updated_at":"2026-07-22T22:24:38Z","subjects":["Space situational awareness","tracking","target search"],"languages":["en"],"rights":["Copyright 2018 Mihir Patel"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101218","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ho, Koki"]},{"key":"dc:creator","label":"Author","values":["Patel, Mihir Jagdishbhai"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:36:53Z","2020-09-05T09:15:13Z","2018-04-25","2018-05"]},{"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":["Space situational awareness","tracking","target search"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Mihir Patel"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101218"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This work studies methods to detect target in an orbit around the Earth using a space based sensor. Searching for a target among a large set of candidate orbits is a difficult and time consuming problem. Considering orbital dynamics, sensor uncertainties and the initial size of candidate location distribution, it is desirable to develop efficient search techniques. In this work, information-theoretic methods for searching a target in a large probability distribution using a space based sensor is considered. One intuitive approach is to steer the sensor towards regions of high probability density. Alternatively, information-theoretic methods steer the sensor based on metrics of the information gain in the posterior probability distribution. Through simulation, it is shown that information-theoretic search methods produce greater knowledge about probability distribution of the target's orbit. We also present methods to lower the computing expense imposed on the computer on-board a space based sensor. The issue is addressed using data clustering technique called K-means clustering. It is shown that errors resulting from searching the target after clustering is much lower compared to errors resulting from searching targets at the locations of higher probability.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01","The student, Mihir Patel, accepted the attached license on 2018-04-24 at 11:35.","The student, Mihir Patel, submitted this Thesis for approval on 2018-04-24 at 11:43.","This Thesis was approved for publication on 2018-04-25 at 09:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12445 on 2018-08-31 at 17:21:23","Made available in DSpace on 2018-09-04T20:36:53Z (GMT). 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Searching for a target among a large set of candidate orbits is a difficult and time consuming problem. Considering orbital dynamics, sensor uncertainties and the initial size of candidate location distribution, it is desirable to develop efficient search techniques. In this work, information-theoretic methods for searching a target in a large probability distribution using a space based sensor is considered. One intuitive approach is to steer the sensor towards regions of high probability density. Alternatively, information-theoretic methods steer the sensor based on metrics of the information gain in the posterior probability distribution. Through simulation, it is shown that information-theoretic search methods produce greater knowledge about probability distribution of the target's orbit. We also present methods to lower the computing expense imposed on the computer on-board a space based sensor. The issue is addressed using data clustering technique called K-means clustering. It is shown that errors resulting from searching the target after clustering is much lower compared to errors resulting from searching targets at the locations of higher probability.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01","The student, Mihir Patel, accepted the attached license on 2018-04-24 at 11:35.","The student, Mihir Patel, submitted this Thesis for approval on 2018-04-24 at 11:43.","This Thesis was approved for publication on 2018-04-25 at 09:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12445 on 2018-08-31 at 17:21:23","Made available in DSpace on 2018-09-04T20:36:53Z (GMT). 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