Hilbert-Huang Transform Code Implementation
Code for Hilbert-Huang Transform analysis of non-stationary signals with empirical mode decomposition and instantaneous frequency extraction
Analysis of LFM Signals Using Fractional Fourier Transform (FRFT)
The Linear Frequency Modulated (LFM) signal, as a typical non-stationary signal with large time-bandwidth product, is widely used in various information systems. From electronic warfare and electronic countermeasures perspectives, LFM signals are commonly employed to resolve the contradiction between operating range and range resolution while enhancing signal concealment. The detection and parameter estimation of LFM signals have become key research focuses in electronic warfare. This MATLAB implementation utilizes Fractional Fourier Transform (FRFT) for accurate LFM parameter estimation, featuring complete code examples with operational results demonstrating effective signal processing capabilities.
Non-Stationary Signal Noise Removal Using Wavelet Transform in MATLAB
MATLAB implementation of wavelet transform for non-stationary signal denoising, including algorithm explanation and key function descriptions
MATLAB Code Implementation of EMD Algorithm Source Code
This is the source code for an Empirical Mode Decomposition (EMD) program that includes edge effect handling, suitable for processing non-stationary signals with enhanced boundary condition management.
General Processing of Non-stationary Signals in Time-Frequency Analysis
This repository contains complete MATLAB code for general processing of non-stationary signals in time-frequency analysis, featuring signal preprocessing, spectral analysis algorithms, and visualization techniques.
Order Analysis Toolkit Package
An order analysis toolkit package designed for non-stationary signal feature extraction, providing efficient algorithms and implementations for frequency-amplitude characteristic analysis.
Measurement and Parameter Estimation of Non-stationary Signals
Time-frequency analysis techniques for measuring and estimating parameters of non-stationary signals, with implementation approaches using signal processing algorithms.
Kalman Filter for Speech Enhancement Algorithms
Kalman filter is applied to speech enhancement algorithms with relatively high computational complexity, making it suitable for processing non-stationary signals. Implementation typically involves state-space modeling and recursive prediction-correction steps.
MATLAB Code Implementation of ARIMA Model
ARIMA Model for Power Spectrum Estimation of Non-Stationary Signals, Including Implementation Approaches and Key Functions
Time-Frequency Analysis of Non-Stationary Signals Based on Short-Time Fourier Transform
Time-frequency analysis of non-stationary signals using Short-Time Fourier Transform (STFT) with implementation insights for signal processing applications