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Analysis, Modeling & Exploitation of Variability in Radar Images
Doo, Seung Ho

2016, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
This dissertation explores the variability in radar measurements that arises due to small changes in target aspect angle, proposes a target modeling approach with augmented point scatterers and developes aspect invariant feature extraction techniques.

First, the causes of the measurement variability are evaluated in quantitive and qualitative manners and attributed to four scatterer types in addition to the pulse compression process. Based on this analysis, an augmented point scatterer model is proposed that allows fast generation of realistic radar data. The proposed model is applied to targets used in the MSTAR data, and its results are compared with actual target measurements. In addition, the analysis of the measurement variability is also used to design feature vectors for target classification that are invariant to target aspect angle. The proposed features are designed to reduce and to exploit variability information in target data. A novel grid cell structure is designed for efficient target information extraction that shows high angular stability by considering the physical structures of potential targets. Lastly, target classification is undertaken, using the proposed feature vectors, demonstrating their utility when working with measured radar data.
Graeme Smith (Advisor)
Joel Johnson (Committee Member)
Robert Burkholder (Committee Member)
160 p.

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Doo, S. (2016). Analysis, Modeling & Exploitation of Variability in Radar Images. (Electronic Thesis or Dissertation). Retrieved from https://etd.ohiolink.edu/

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Doo, Seung Ho. "Analysis, Modeling & Exploitation of Variability in Radar Images." Electronic Thesis or Dissertation. Ohio State University, 2016. OhioLINK Electronic Theses and Dissertations Center. 23 Nov 2017.

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Doo, Seung Ho "Analysis, Modeling & Exploitation of Variability in Radar Images." Electronic Thesis or Dissertation. Ohio State University, 2016. https://etd.ohiolink.edu/

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