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Performance analysis and algorithm enhancement of feature-aided-tracker (FAT) simulation software using 1-D high-range-resolution (HRR) radar signature profiles

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dc.contributor.advisor Derek Rowell. en_US
dc.contributor.author O'Brien, Michael J. (Michael James), 1981- en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Mechanical Engineering. en_US
dc.date.accessioned 2006-03-24T18:38:50Z
dc.date.available 2006-03-24T18:38:50Z
dc.date.copyright 2005 en_US
dc.date.issued 2005 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/30309
dc.description Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005. en_US
dc.description Includes bibliographical references (p. 94). en_US
dc.description.abstract The current Lincoln Laboratory (LL) MATLAB Feature-Aided-Tracker (FAT) software was adjusted and appended to provide a robust ground-target radar tracking simulation tool. It utilizes algorithms from the LL UAV Radar Moving Target Tracker (1991) and the LL FAT Tracking Software (2002). One-dimensional High-Range-Resolution (HRR) radar signature target profiles were used to assist in track-to-report data association through classification-aided and signature-aided tracking (CAT and SAT) algorithms. Profiles were obtained from the DARPA-sponsored Moving Target Feature Phenomenology (MTFP) program. Performance Analysis of this simulation tool reinforced the hypothesis that target aspect angle error estimation (state estimation) drives the performance of CAT, SAT, and Kinematic Tracking (KT) algorithms. A decaying exponential relationship exists between the Kalman filter estimate of target-speed and expected aspect angle error. This relationship was exploited to optimize the allocation of computational resources while enlarging the database aspect angle search in CAT to improve performance. Vehicle classification accuracy is improved by 70% and data association accuracy is improved by 12% in kinematically ambiguous situations such as when target intersections occur. SAT was improved 3% using this knowledge. Additionally, the target report HRR profile from each scan was used to generate an "On-The- Fly" SAT HRR profile database. This algorithm tests the similarity between the current target report HRR profile and the database HRR profiles. If there is sufficient resemblance, the report HRR is added to the database; if not, the database is reset. en_US
dc.description.abstract (cont.) This information can be employed to provide up to a 9% performance improvement over the previous version of SAT in a best-case scenario. In realistic situations, a 6% performance improvement is still attainable. If a large, accurate database exists, near-perfect data association is achieved. Overall, the above technique adjustments provide an improvement of 6.3% (13.6% in realistic, GPS-generated scenarios) in data association accuracy over the initial FAT algorithm and a corresponding 28.8% improvement over the results of the KT itself. en_US
dc.description.statementofresponsibility by Michael J. O'Brien. en_US
dc.format.extent 94 p. en_US
dc.format.extent 7133267 bytes
dc.format.extent 7143866 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/pdf
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Mechanical Engineering. en_US
dc.title Performance analysis and algorithm enhancement of feature-aided-tracker (FAT) simulation software using 1-D high-range-resolution (HRR) radar signature profiles en_US
dc.type Thesis en_US
dc.description.degree S.M. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Mechanical Engineering. en_US
dc.identifier.oclc 61050365 en_US


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