{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/72800"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/72800","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A distributed local Kalman consensus filter for traffic estimation: design, analysis and validation","abstract":"This thesis proposes a distributed local Kalman consensus filter (DLKCF) for large-scale multi-agent traffic density estimation. The switching mode model (SMM) describes the traffic dynamics on a stretch of roadway, and the model dynamics are linear within each mode. The error dynamics of the proposed DLKCF is shown to be globally asymptotically stable (GAS) when all freeway sections switch between observable modes. For an unobservable section, the estimates given by the DLKCF are proved to be ultimately bounded. We also show that under some frequently encountered conditions, the error sum in an unobservable section converges to a fixed value. Numerical experiments verify the asymptotic stability of the DLKCF for observable modes, compare the DLKCF to a Luenberger observer, illustrate the capability of the DLKCF on promoting consensus among various local agents, and show a considerable reduction of the runtime of the DLKCF compared to a central KF. Supplementary source code is available to be downloaded at https://github.com/yesun/DLKCFthesis.","abstract_html":"This thesis proposes a distributed local Kalman consensus filter (DLKCF) for large-scale multi-agent traffic density estimation. The switching mode model (SMM) describes the traffic dynamics on a stretch of roadway, and the model dynamics are linear within each mode. The error dynamics of the proposed DLKCF is shown to be globally asymptotically stable (GAS) when all freeway sections switch between observable modes. For an unobservable section, the estimates given by the DLKCF are proved to be ultimately bounded. We also show that under some frequently encountered conditions, the error sum in an unobservable section converges to a fixed value. Numerical experiments verify the asymptotic stability of the DLKCF for observable modes, compare the DLKCF to a Luenberger observer, illustrate the capability of the DLKCF on promoting consensus among various local agents, and show a considerable reduction of the runtime of the DLKCF compared to a central KF. Supplementary source code is available to be downloaded at https://github.com/yesun/DLKCFthesis.","abstract_has_math":false,"creators":["Sun, Ye"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Work, Daniel B."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-01-21T19:48:19Z","date_published":"2015-01-21T19:48:19Z","updated_at":"2026-07-22T22:26:07Z","subjects":["Traffic state estimation","hybrid systems","observability","distributed Kalman filter","consensus filter"],"languages":["en"],"rights":["Copyright 2014 Ye Sun"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/72800","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Work, Daniel B."]},{"key":"dc:creator","label":"Author","values":["Sun, Ye"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-01-21T19:48:19Z","2014-12","2015-01-21"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil 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":["Traffic state estimation","hybrid systems","observability","distributed Kalman filter","consensus 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 2014 Ye Sun"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/72800"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis proposes a distributed local Kalman consensus filter (DLKCF) for large-scale multi-agent traffic density estimation. The switching mode model (SMM) describes the traffic dynamics on a stretch of roadway, and the model dynamics are linear within each mode. The error dynamics of the proposed DLKCF is shown to be globally asymptotically stable (GAS) when all freeway sections switch between observable modes. For an unobservable section, the estimates given by the DLKCF are proved to be ultimately bounded. We also show that under some frequently encountered conditions, the error sum in an unobservable section converges to a fixed value. Numerical experiments verify the asymptotic stability of the DLKCF for observable modes, compare the DLKCF to a Luenberger observer, illustrate the capability of the DLKCF on promoting consensus among various local agents, and show a considerable reduction of the runtime of the DLKCF compared to a central KF. Supplementary source code is available to be downloaded at https://github.com/yesun/DLKCFthesis.","Item withdrawn by Laura Spradlin (lspradl2@illinois.edu) on 2014-12-05T17:45:49Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Sun_Ye.tex: 2000 bytes, checksum: bf6ce0c6e4e66ea76b2b10831d684b09 (MD5) Sun_Ye.pdf: 2603166 bytes, checksum: ddaaa00f7fc32099fe29a6fd703ea27e (MD5)","Made available in DSpace on 2015-01-21T19:48:19Z (GMT). No. of bitstreams: 2 Ye_Sun.pdf: 2603166 bytes, checksum: ddaaa00f7fc32099fe29a6fd703ea27e (MD5) Sun_Ye.tex: 2000 bytes, checksum: bf6ce0c6e4e66ea76b2b10831d684b09 (MD5)"]},{"key":"dc:title","label":"Title","values":["A distributed local Kalman consensus filter for traffic estimation: design, analysis and validation"]}]}],"canonical_facts":{"dc:contributor":["Work, Daniel B."],"dc:creator":["Sun, Ye"],"dc:date":["2015-01-21T19:48:19Z","2014-12","2015-01-21"],"dc:description":["This thesis proposes a distributed local Kalman consensus filter (DLKCF) for large-scale multi-agent traffic density estimation. The switching mode model (SMM) describes the traffic dynamics on a stretch of roadway, and the model dynamics are linear within each mode. The error dynamics of the proposed DLKCF is shown to be globally asymptotically stable (GAS) when all freeway sections switch between observable modes. For an unobservable section, the estimates given by the DLKCF are proved to be ultimately bounded. We also show that under some frequently encountered conditions, the error sum in an unobservable section converges to a fixed value. Numerical experiments verify the asymptotic stability of the DLKCF for observable modes, compare the DLKCF to a Luenberger observer, illustrate the capability of the DLKCF on promoting consensus among various local agents, and show a considerable reduction of the runtime of the DLKCF compared to a central KF. Supplementary source code is available to be downloaded at https://github.com/yesun/DLKCFthesis.","Item withdrawn by Laura Spradlin (lspradl2@illinois.edu) on 2014-12-05T17:45:49Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Sun_Ye.tex: 2000 bytes, checksum: bf6ce0c6e4e66ea76b2b10831d684b09 (MD5) Sun_Ye.pdf: 2603166 bytes, checksum: ddaaa00f7fc32099fe29a6fd703ea27e (MD5)","Made available in DSpace on 2015-01-21T19:48:19Z (GMT). No. of bitstreams: 2 Ye_Sun.pdf: 2603166 bytes, checksum: ddaaa00f7fc32099fe29a6fd703ea27e (MD5) Sun_Ye.tex: 2000 bytes, checksum: bf6ce0c6e4e66ea76b2b10831d684b09 (MD5)"],"dc:identifier":["http://hdl.handle.net/2142/72800"],"dc:language":["en"],"dc:rights":["Copyright 2014 Ye Sun"],"dc:subject":["Traffic state estimation","hybrid systems","observability","distributed Kalman filter","consensus filter"],"dc:title":["A distributed local Kalman consensus filter for traffic estimation: design, analysis and validation"],"dc:type":["text"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:07Z"}