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On the Implmentation of the Strip Spectral Correlation Algorithm for Cyclic Spectrum Estimation.
DEFENCE RESEARCH ESTABLISHMENT OTTAWA (ONTARIO)
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This report discusses the implementation of one of the best digital cyclic spectrum CS algorithms derived so far, the Strip Spectral Correlation Algorithm SSCA. Some theoretical background and a detailed description of the SSCA are provided. An analysis of the SSCA is performed and an algorithm for mapping the SSCA output is formulated. The cyclic feature function CFF is defined as a means to detect the cyclic features from the SSCA. Results of the SSCA encoded in C are then reported. Three BPSK signals and two additive white Gaussian noise AWGN signals are used to verify the validity of the SSCA. Three-dimensional plots and two-dimensional plots of the CS and CFF respectively are presented to the reader. Finally, some benchmarks on a SUN computer for the SSCA are provided for reference. In brief, the CS and CFF estimated with the SSCA prove to be valuable tools for analyzing second-order cyclostationary communication signals and, by making extensive use of the FFT, to provide robust, reliable, and accurate results more efficiently than typical CS direct estimation methods.
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