Type of Document Master's Thesis Author Malady, Amy Colleen URN etd-05062011-111048 Title Cyclostationarity Feature-Based Detection and Classification Degree Master of Science Department Electrical and Computer Engineering Advisory Committee
Advisor Name Title Beex, A. A. Louis Committee Chair Bose, Tamal Committee Member Meehan, Kathleen Committee Member Keywords
- robust estimation
- continuous phase modulation
- automatic modulation classification
Date of Defense 2011-04-22 Availability unrestricted AbstractCyclostationarity feature-based (C-FB) detection and classification is a large field of research that has promising applications to intelligent receiver design. Cyclostationarity FB classification and detection algorithms have been applied to a breadth of wireless communication signals – analog and digital alike. This thesis reports on an investigation of existing methods of extracting cyclostationarity features and then presents a novel robust solution that reduces SNR requirements, removes the pre-processing task of estimating occupied signal bandwidth, and can achieve classification rates comparable to those achieved by the traditional method while based on only 1/10 of the observation time. Additionally, this thesis documents the development of a novel low order consideration of the cyclostationarity present in Continuous Phase Modulation (CPM) signals, which is more practical than using higher order cyclostationarity.
Results are presented – through MATLAB simulation – that demonstrate the improvements enjoyed by FB classifiers and detectors when using robust methods of estimating cyclostationarity. Additionally, a MATLAB simulation of a CPM C-FB detector confirms that low order C-FB detection of CPM signals is possible. Finally, suggestions for further research and contribution are made at the conclusion of the thesis.
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