Explore MATLAB source code curated for "参数估计" with clean implementations, documentation, and examples.
This MATLAB implementation addresses defocused blur images by first estimating Point Spread Function (PSF) parameters using cepstral correlation analysis, followed by image restoration through Wiener filtering. The algorithm effectively enhances image quality by reducing blur artifacts and improving visual clarity.
Time-frequency analysis techniques for measuring and estimating parameters of non-stationary signals, with implementation approaches using signal processing algorithms.
This simulation code demonstrates Linear Frequency Modulated (LFM) signal parameter estimation through Wigner-Hough Transform (WHT). The implementation builds upon a time-frequency analysis package with customized modifications, featuring detailed algorithm design including signal generation, WHT computation, and peak detection for parameter extraction.
A MATLAB-based cyclostationary spectrum estimation algorithm implementation for parameter estimation of signals exhibiting cyclostationary properties, featuring computational approaches and spectral analysis techniques.
Cyclic spectrum estimation algorithm implemented in MATLAB for parameter estimation of cyclostationary signals, including direct-sequence spread spectrum signals and periodic modulation signals, featuring FFT-based spectral correlation computation and cyclic frequency analysis.
This code implements signal recovery in compressed sensing theory by transforming it into a regression problem with parameter constraints. Through Bayesian parameter estimation techniques, it achieves efficient reconstruction of sparse signals. The implementation includes key components for optimization algorithms and sparse modeling.
Implementation of parameter estimation using unweighted recursive least squares method for system identification and simulation, featuring algorithm explanation and key MATLAB function descriptions
This MATLAB implementation demonstrates parameter estimation for Gaussian Mixture Models via the Expectation-Maximization algorithm, featuring clear code structure with detailed comments for easy understanding of the iterative optimization process.
Implementation of Unscented Kalman Filter (UKF) for joint state and parameter estimation in nonlinear systems with nonlinear measurement and process noise characteristics
This EM algorithm implementation specializes in parameter estimation for Gaussian Mixture Models (GMM), featuring iterative E-step and M-step operations for optimal parameter convergence.