Automatic Target Recognition (ATR) is a subject involving the use of sensor data to develop an algorithm for identifying targets of significance. It is of particular interest in military applications such as unmanned aerial vehicles and missile tracking systems. This thesis develops an orientation-based classification approach from previous ATR algorithms for 2-D Synthetic Aperture Radar (SAR) images. Prior work in ATR includes Chessa Guilas’ Hausdorff Probabilistic Feature Analysis Approach in 2005 and Daniel Cary’s Optimal Rectangular Fit in 2007.
A system incorporating multiple modules performing different tasks is developed to streamline the data processing of previous algorithms. Using images from the publicly available Moving and Stationary Target Acquisition and Recognition (MSTAR) database, target orientation was determined to be the best feature for ATR. A rotationally variant algorithm taking advantage of the combination of target orientation and pixel location for classification is proposed in this thesis. Extensive classification results yielding an overall accuracy of 76.78% are presented to demonstrate algorithm functionality.
","abstract_html":"<p>Automatic Target Recognition (ATR) is a subject involving the use of sensor data to develop an algorithm for identifying targets of significance. It is of particular interest in military applications such as unmanned aerial vehicles and missile tracking systems. This thesis develops an orientation-based classification approach from previous ATR algorithms for 2-D Synthetic Aperture Radar (SAR) images. Prior work in ATR includes Chessa Guilas’ Hausdorff Probabilistic Feature Analysis Approach in 2005 and Daniel Cary’s Optimal Rectangular Fit in 2007.</p> <p>A system incorporating multiple modules performing different tasks is developed to streamline the data processing of previous algorithms. Using images from the publicly available Moving and Stationary Target Acquisition and Recognition (MSTAR) database, target orientation was determined to be the best feature for ATR. A rotationally variant algorithm taking advantage of the combination of target orientation and pixel location for classification is proposed in this thesis. Extensive classification results yielding an overall accuracy of 76.78% are presented to demonstrate algorithm functionality.</p>","abstract_has_math":false,"creators":["Kuo, Justin Ting-Jeuan"],"institution":null,"degree_name":"MS in Electrical Engineering","degree_level":null,"degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["John A. Saghri"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-06-01T07:00:00Z","date_published":"2014-06-01T07:00:00Z","updated_at":"2026-07-24T01:31:28Z","subjects":["Automatic Target Recognition","Synthetic Aperture Radar","Image Processing","Machine Learning","MSTAR","Other Electrical and Computer Engineering","Signal Processing"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2014.75"],"render_values":[{"text":"10.15368/theses.2014.75","href":"https://doi.org/10.15368/theses.2014.75","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1229","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["John A. Saghri"]},{"key":"dc:creator","label":"Author","values":["Kuo, Justin Ting-Jeuan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2014-06-16T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Electrical Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Automatic Target Recognition","Synthetic Aperture Radar","Image Processing","Machine Learning","MSTAR","Other Electrical and Computer Engineering","Signal Processing"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1229","10.15368/theses.2014.75"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Automatic Target Recognition (ATR) is a subject involving the use of sensor data to develop an algorithm for identifying targets of significance. It is of particular interest in military applications such as unmanned aerial vehicles and missile tracking systems. This thesis develops an orientation-based classification approach from previous ATR algorithms for 2-D Synthetic Aperture Radar (SAR) images. Prior work in ATR includes Chessa Guilas’ Hausdorff Probabilistic Feature Analysis Approach in 2005 and Daniel Cary’s Optimal Rectangular Fit in 2007.
A system incorporating multiple modules performing different tasks is developed to streamline the data processing of previous algorithms. Using images from the publicly available Moving and Stationary Target Acquisition and Recognition (MSTAR) database, target orientation was determined to be the best feature for ATR. A rotationally variant algorithm taking advantage of the combination of target orientation and pixel location for classification is proposed in this thesis. Extensive classification results yielding an overall accuracy of 76.78% are presented to demonstrate algorithm functionality.
"]},{"key":"dc:title","label":"Title","values":["ATREngine: An Orientation-Based Algorithm for Automatic Target Recognition"]}]}],"canonical_facts":{"dc:contributor":["John A. Saghri"],"dc:creator":["Kuo, Justin Ting-Jeuan"],"dc:date.available":["2014-06-16T07:00:00Z"],"dc:description.abstract":["Automatic Target Recognition (ATR) is a subject involving the use of sensor data to develop an algorithm for identifying targets of significance. It is of particular interest in military applications such as unmanned aerial vehicles and missile tracking systems. This thesis develops an orientation-based classification approach from previous ATR algorithms for 2-D Synthetic Aperture Radar (SAR) images. Prior work in ATR includes Chessa Guilas’ Hausdorff Probabilistic Feature Analysis Approach in 2005 and Daniel Cary’s Optimal Rectangular Fit in 2007.
A system incorporating multiple modules performing different tasks is developed to streamline the data processing of previous algorithms. Using images from the publicly available Moving and Stationary Target Acquisition and Recognition (MSTAR) database, target orientation was determined to be the best feature for ATR. A rotationally variant algorithm taking advantage of the combination of target orientation and pixel location for classification is proposed in this thesis. Extensive classification results yielding an overall accuracy of 76.78% are presented to demonstrate algorithm functionality.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/1229","10.15368/theses.2014.75"],"dc:subject":["Automatic Target Recognition","Synthetic Aperture Radar","Image Processing","Machine Learning","MSTAR","Other Electrical and Computer Engineering","Signal Processing"],"dc:title":["ATREngine: An Orientation-Based Algorithm for Automatic Target Recognition"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_name":["MS in Electrical Engineering"]},"updated_at":"2026-07-24T01:31:28Z"}