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Application of Dual-Tree Complex Wavelet Transforms to Burst Detection and RF Fingerprint Classification
AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH GRADUATE SCHOOL OF ENGINEERING AND MANAGEMENT
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This work addresses various Open Systems Interconnection OSI Physical PHY layer mechanisms to extract and exploit RF waveform features fingerprints that are inherently unique to specific devices and that may be used to provide hardware specific identification manufacturer, model, andor serial number. This is addressed by applying a Dual-Tree Complex Wavelet Transform DT-CWT to improve burst detection and RF fingerprint classification. A Denoised VT technique is introduced to improve performance at lower SNRs, with denoising implemented using a DT-CWT decomposition prior to Traditional VT processing. A newly developed Wavelet Domain WD fingerprinting technique is presented using statistical WD fingerprints with Multiple Discriminant AnalysisMaximum Likelihood MDAML classification. The statistical fingerprint features are extracted from coefficients of a DT-CWT decomposition. Relative to previous Time Domain TD results, the enhanced WD statistical features provide improved device classification performance. Additional performance sensitivity results are presented to demonstrate WD fingerprinting robustness for variation in burst location error, MDAML training and classification SNRs, and MDAML training and classification signal types. For all cases considered, the WD technique proved to be more robust and exhibited less sensitivity when compared with the TD Technique.
APPROVED FOR PUBLIC RELEASE