@inbook {IOPORT.00834338,
    author       = {Roberts, Randy S. and Brown, William A. and Loomis, Herschel H.jun.},
    title        = {A review of digital spectral correlation analysis: Theory and implementation.},
    year         = {1994},
    booktitle    = {Cyclostationarity in communications and signal processing},
    isbn         = {0-7803-1023-3},
    pages        = {455-479},
    publisher    = {New York, NY: IEEE},
    abstract     = {This article reviews the theory behind digital spectral correlation analysis algorithms and describes various aspects of implementing the algorithms. The discussion begins with time smoothing algorithms. In general, FFT based time smoothing algorithms are considered most attractive for computing estimates of the spectral correlation function over the entire bifrequency plane. Two computationally efficient algorithms, the FFT Accumulation Method and the Strip Spectral Correlation Algorithm, are described here. Frequency smoothing methods are generally considered best for computing estimates of the spectral correlation function along lines of constant cycle frequency for moderate numbers of cycle frequencies. In particular, a frequency smoothing algorithm called the Digital Frequency Smoothing Method is useful for this type of estimation problem. Although spectral correlation algorithms are generally classified as time or frequency smoothing, hybrid algorithms (i.e., algorithms that smooth both in time and in frequency) can be advantageous in certain applications.},
    identifier   = {00834338},
}