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dc.contributor.advisorPace, Phillip E.
dc.contributor.authorZilberman, Eric R.
dc.date.accessioned2012-03-14T17:35:59Z
dc.date.available2012-03-14T17:35:59Z
dc.date.issued2006-06
dc.identifier.urihttp://hdl.handle.net/10945/2700
dc.description.abstractThree autonomous cropping and feature extraction algorithms are examined that can be used for classification of low probability of intercept radar modulations using time-frequency (T-F) images. The first approach, Erosion Dilation Adaptive Binarization (EDAB), uses erosion and a new adaptive threshold binarization algorithm embedded within a recursive dilation process to determine the modulation energy centroid (radar's carrier frequency) and properly place a fixed-width cropping window. The second approach, Marginal Frequency Adaptive Binarization (MFAB), uses the marginal frequency distribution and the adaptive threshold binarization algorithm to determine the start and stop frequencies of the modulation energy to locate and adapt the size of the cropping window. The third approach, Fast Image Filtering, uses the fast Fourier transform and a Gaussian lowpass filter to isolate the modulation energy. The modulation is then cropped from the original T-F image and the adaptive binarization algorithm is used again to compute a binary feature vector for input into a classification network. The binary feature vector allows the image detail to be preserved without overwhelming the classification network that follows. A multi-layer perceptron and a radial basis function network are used for classification and the results are compared. Classification results for nine simulated radar modulations are shown to demonstrate the three feature-extraction approaches and quantify the performance of the algorithms. It is shown that the best results are obtained using the Choi-Williams distribution followed by the MFAB algorithm and a multi-layer perceptron. This setup produced an overall percent correct classification (Pcc) of 87.2% for testing with noise variation and 77.8% for testing with modulation variation. In an operational context, the ability to process and classify LPI signals autonomously allows the operator in the field to receive real-time results.en_US
dc.format.extentxiv, 78 p. : ill. (some col.) ;en_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.en_US
dc.subject.lcshClassificationen_US
dc.subject.lcshSignal processingen_US
dc.subject.lcshAlgorithmsen_US
dc.subject.lcshTechnologyen_US
dc.subject.lcshRadaren_US
dc.titleAutonomous time-frequency cropping and feature-extraction algorithms for classification of LPI radar modulationsen_US
dc.typeThesisen_US
dc.contributor.secondreaderBrutzman, Don
dc.contributor.corporateNaval Postgraduate School (U.S.).
dc.contributor.departmentInformation Sciences (IS)
dc.identifier.oclc70670376
etd.thesisdegree.nameM.S.en_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineSystems Technologyen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
etd.verifiednoen_US


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