Explore MATLAB source code curated for "谐波恢复" with clean implementations, documentation, and examples.
A harmonic recovery approach utilizing fourth-order cumulants demonstrates advantages in noise suppression and harmonic estimation, with practical implementation involving statistical signal processing techniques.
A simulation program from "Modern Signal Processing" that demonstrates parameter estimation for AutoRegressive Moving Average (ARMA) models using least squares method, including harmonic recovery implementation with MATLAB code examples and algorithm explanations.
This code implements the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm, which has become a principal eigenvalue decomposition method for harmonic recovery - specifically estimating the number of sinusoidal components and their frequencies. The implementation utilizes matrix subspace decomposition and rotational invariance properties for high-resolution frequency estimation.
ARMA modeling algorithm for harmonic retrieval based on SVD-TLS (from Zhang Xianda's "Modern Signal Processing"). This algorithm remains applicable for complex harmonic signals. The program requires implementation of key functions including matrix construction, singular value decomposition, and total least squares solution.
Program implementation of SVD-TLS based ESPRIT method for harmonic retrieval, featuring simulation experiments on signal harmonic recovery with analysis of algorithm performance and comparison metrics
Implementation of harmonic recovery using the MUSIC algorithm, which achieves high precision with frequency resolution capability of 0.013Hz. The algorithm utilizes eigenvalue decomposition of the covariance matrix and pseudospectrum analysis for accurate frequency estimation.
Least Squares Estimation of ARMA Model Parameters with Algorithm Implementation Details