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S3CA: A Sparse Strip Spectral Correlation Analyzer
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DOI：10.1109/LSP.2024.3364062.png)
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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 (S(3)CA) based on the sparse fast Fourier transform (SFFT). The S(3)CA 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 S(3)CA is 30x faster than the conventional SSCA.
Keyword:
Correlation
Strips
Time-frequency analysis
Fast Fourier transforms
Signal processing algorithms
Time-domain analysis
Frequency estimation
Cyclostationarity
fast Fourier transform
spectral correlation density
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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