The probability density function of spectral correlation function estimates.

Saved in:
Bibliographic Details
Title: The probability density function of spectral correlation function estimates.
Authors: Tavares, Miguel, Gerald, José, Goes, João
Source: EURASIP Journal on Advances in Signal Processing; 10/30/2025, Vol. 2025 Issue 1, p1-24, 24p
Subject Terms: PROBABILITY density function, STATISTICS, QUADRATIC forms, SPECTRAL energy distribution, GAUSSIAN distribution, SIGNAL processing
Abstract: Since published in 1988, the FFT Accumulation Method (FAM) has been used extensively to compute the Spectral Correlation Function (SCF) and the Spectral Coherence Function (SCoF) to obtain or detect cyclic features of cyclostationary signals. When the input is a Gaussian random variable (r.v.), the SCF (or SCoF) estimates are also random variables with some probability density function (pdf). Although the FAM is considered the most computationally efficient method, there has been no in-depth statistical analysis of the algorithm. This paper analyzes the statistics of spectral estimates of the SCF using the FAM algorithm by obtaining the pdf for the points covering the frequency and cycle frequency f ; α plane, and application examples with simulation results are provided. The method proposed in the paper can be extended to other algorithms, provided they can be given by a quadratic form. [ABSTRACT FROM AUTHOR]
Copyright of EURASIP Journal on Advances in Signal Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
Be the first to leave a comment!
You must be logged in first