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Target Discrimination Against Clutter Based on Unsupervised Clustering and Sequential Monte Carlo Tracking


Abstract The radar performance of detecting a target and estimating its parameters can deteriorate rapidly in the presence of high clutter. This is because radar measurements due to clutter returns can be falsely detected as if originating from the actual target. Various data association methods and multiple hypothesis filtering approaches have been considered to solve this problem. Such methods, however, can be computationally intensive for real time radar processing. This work proposes a new approach that is based on the unsupervised clustering of target and clutter detections before target tracking using particle filtering. In particular, Gaussian mixture modeling is first used to separate detections into two Gaussian distinct mixtures. Using ... (more)
Created Date 2016
Contributor Freeman, Matthew Gregory (Author) / Papandreou-Suppappola, Antonia (Advisor) / Bliss, Daniel (Advisor) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / Systems science / Clutter / Machine Learning / Radar / Sequential Monte Carlo Methods / Target Tracking / Unsupervised Clustering
Type Masters Thesis
Extent 62 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Masters Thesis Electrical Engineering 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis