Explore MATLAB source code curated for "相关函数" with clean implementations, documentation, and examples.
Generating narrowband random processes based on Rice representation of stochastic processes, including mastering their key characteristics such as mean (mathematical expectation), variance, correlation function, and power spectral density through implementation approaches involving random variable generation and spectral analysis techniques.
White Noise Testing in Random Signal Analysis Experiments: Characteristics of white noise signals including mean, variance, correlation function, probability density, frequency spectrum, and power spectral density, with implementation approaches using signal processing functions.
This resource presents both standard and optimized fast algorithms for calculating correlation functions of cyclostationary signals, complete with implementation examples and practical applications to assist researchers and engineers.
Computing signal power spectrum through correlation functions, including autocorrelation for signal self-similarity analysis and cross-correlation for inter-signal relationship assessment.
Generate a random signal and two sinusoidal signals with different but closely-spaced frequencies, then perform comprehensive signal analysis including: (1) calculating autocorrelation coefficients and correlation functions with corresponding plots; (2) computing power spectra using different parametric modeling methods; (3) estimating parameters for AR, MA, and ARMA models using maximum likelihood estimation and recursive least squares, with comparison to MATLAB toolbox functions; (4) spectral estimation using notch filtering and MUSIC methods; (5) noise reduction using Wiener and LMS filtering
This concise correlation function implementation for cyclostationary processes provides excellent learning material for beginners, featuring straightforward code structure and clear algorithm implementation.