{"id":{"repo_id":"uno","oai_identifier":"oai:scholarworks.uno.edu:td-1019"},"canonical_url":"https://search.dev.ndltd.org/etd/uno/oai:scholarworks.uno.edu:td-1019","repository":{"repo_id":"uno","name":"University of New Orleans","base_url":"https://scholarworks.uno.edu/do/oai/"},"display":{"title":"Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking","abstract":"Nonlinear filtering is certainly very important in estimation since most real-world problems are nonlinear. Recently a considerable progress in the nonlinear filtering theory has been made in the area of the sampling-based methods, including both random (Monte Carlo) and deterministic (quasi-Monte Carlo) sampling, and their combination. This work considers the problem of tracking a maneuvering target in a multisensor environment. A novel scheme for distributed tracking is employed that utilizes a nonlinear target model and estimates from local (sensor-based) estimators. The resulting estimation problem is highly nonlinear and thus quite challenging. In order to evaluate the performance capabilities of the architecture considered, advanced sampling-based nonlinear filters are implemented: particle filter (PF), unscented Kalman filter (UKF), and unscented particle filter (UPF). Results from extensive Monte Carlo simulations using different configurations of these algorithms are obtained to compare their effectiveness for solving the distributed target tracking problem.","abstract_html":"Nonlinear filtering is certainly very important in estimation since most real-world problems are nonlinear. Recently a considerable progress in the nonlinear filtering theory has been made in the area of the sampling-based methods, including both random (Monte Carlo) and deterministic (quasi-Monte Carlo) sampling, and their combination. This work considers the problem of tracking a maneuvering target in a multisensor environment. A novel scheme for distributed tracking is employed that utilizes a nonlinear target model and estimates from local (sensor-based) estimators. The resulting estimation problem is highly nonlinear and thus quite challenging. In order to evaluate the performance capabilities of the architecture considered, advanced sampling-based nonlinear filters are implemented: particle filter (PF), unscented Kalman filter (UKF), and unscented particle filter (UPF). Results from extensive Monte Carlo simulations using different configurations of these algorithms are obtained to compare their effectiveness for solving the distributed target tracking problem.","abstract_has_math":false,"creators":["Nguyen, Trang"],"institution":null,"degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["Li, Xiao-Rong","Chen, Humin","Jilkov, Vesselin"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2003,"date_issued":"2003-05-16T07:00:00Z","date_published":"2003-05-16T07:00:00Z","updated_at":"2026-07-24T05:27:57Z","subjects":["statistical inference"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uno.edu/td/20","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Li, Xiao-Rong","Chen, Humin","Jilkov, Vesselin"]},{"key":"dc:creator","label":"Author","values":["Nguyen, Trang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["statistical inference"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uno.edu/td/20"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Nonlinear filtering is certainly very important in estimation since most real-world problems are nonlinear. Recently a considerable progress in the nonlinear filtering theory has been made in the area of the sampling-based methods, including both random (Monte Carlo) and deterministic (quasi-Monte Carlo) sampling, and their combination. This work considers the problem of tracking a maneuvering target in a multisensor environment. A novel scheme for distributed tracking is employed that utilizes a nonlinear target model and estimates from local (sensor-based) estimators. The resulting estimation problem is highly nonlinear and thus quite challenging. In order to evaluate the performance capabilities of the architecture considered, advanced sampling-based nonlinear filters are implemented: particle filter (PF), unscented Kalman filter (UKF), and unscented particle filter (UPF). Results from extensive Monte Carlo simulations using different configurations of these algorithms are obtained to compare their effectiveness for solving the distributed target tracking problem."]},{"key":"dc:title","label":"Title","values":["Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking"]}]}],"canonical_facts":{"dc:contributor":["Li, Xiao-Rong","Chen, Humin","Jilkov, Vesselin"],"dc:creator":["Nguyen, Trang"],"dc:description.abstract":["Nonlinear filtering is certainly very important in estimation since most real-world problems are nonlinear. Recently a considerable progress in the nonlinear filtering theory has been made in the area of the sampling-based methods, including both random (Monte Carlo) and deterministic (quasi-Monte Carlo) sampling, and their combination. This work considers the problem of tracking a maneuvering target in a multisensor environment. A novel scheme for distributed tracking is employed that utilizes a nonlinear target model and estimates from local (sensor-based) estimators. The resulting estimation problem is highly nonlinear and thus quite challenging. In order to evaluate the performance capabilities of the architecture considered, advanced sampling-based nonlinear filters are implemented: particle filter (PF), unscented Kalman filter (UKF), and unscented particle filter (UPF). Results from extensive Monte Carlo simulations using different configurations of these algorithms are obtained to compare their effectiveness for solving the distributed target tracking problem."],"dc:identifier":["https://scholarworks.uno.edu/td/20"],"dc:subject":["statistical inference"],"dc:title":["Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."]},"updated_at":"2026-07-24T05:27:57Z"}