Explore MATLAB source code curated for "参数估计" with clean implementations, documentation, and examples.
MATLAB simulation implementation of fractional Fourier transform for detecting and estimating parameters of single-component linear frequency modulation (LFM) signals, featuring algorithm optimization and performance analysis code descriptions.
Implementation of SVD-TLS Algorithm for Parameter Estimation: This paper employs the SVD-TLS algorithm to estimate the power spectrum of observed signals through three key computational steps: (1) Compute SVD of the sample correlation function matrix R and store singular values with matrix V, (2) Determine the effective rank of R using normalized singular value method, (3) Estimate parameters via Total Least Squares approach.
Code for distribution fitting with Generalized Gaussian Model (GGM), including histogram generation, parameter estimation algorithms, and model optimization techniques
Parameter estimation techniques for near-field signals, including signal direction and range, with implementation examples using array processing algorithms
A MATLAB implementation of the RANSAC parameter estimation algorithm with comprehensive testing images for validation and demonstration purposes
A classic EM algorithm implementation for parameter estimation in Gaussian mixture distribution models, including core algorithmic steps and MATLAB code demonstration. Designed to assist with statistical modeling and machine learning applications.
This MATLAB implementation of the Constrained Maximum Expectation algorithm provides robust parameter estimation and image segmentation capabilities, featuring Bayesian inference methods with prior information integration.
MATLAB Simulation Implementation of Fractional Fourier Transform for Detection and Parameter Estimation of Single Component Linear Frequency Modulated Signals
The EM (Expectation-Maximization) algorithm is a widely used approach for parameter estimation, serving as an alternative to maximum likelihood estimation when dealing with incomplete data samples. It involves iterative optimization steps that progressively refine parameter values through expectation (E-step) and maximization (M-step) phases.
This program implements the Expectation-Maximization (EM) algorithm, providing a numerical solution for parameter estimation using maximum likelihood techniques.