S$^{3}$CA: A Sparse Strip Spectral Correlation Analyzer
Carol Jingyi Li, Richard Rademacher, David Boland, Craig T Jin, Chad M. Spooner, Philip H. W. Leong · IEEE Signal Processing Letters · 2024
The spectral correlation density (SCD) is widely used to characterize cyclostationary signals and the strip spectral correlation analyzer (SSCA) is commonly used to estimate the SCD. Although the SSCA utilizes the fast Fourier transform (FFT) for computational efficiency, its real-time implementation still poses challenges as large input sizes are often involved. In this work, we present a sparse strip spectral correlation analyzer (S3CA) based on the sparse fast Fourier transform (SFFT). The S3CA approach involves computing a sparse, downsampled channel-data product (CDP) which is then passed to a modified SFFT implementation to obtain the spectral density. For an input of length 2 million samples, the S3CA is 30× faster than the conventional SSCA.