Simulating White Noise and Rayleigh-Distributed Noise as Signal Background Noise

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Resource Overview

Simulation of white noise and Rayleigh-distributed noise as signal background noise, followed by background noise denoising analysis using wavelet denoising, EMD denoising, and combined EMD-wavelet denoising methods with code implementation details.

Detailed Documentation

This study aims to analyze signal background noise using various denoising methods, including simulation of white noise and Rayleigh-distributed noise. We will perform background noise denoising analysis through wavelet denoising, Empirical Mode Decomposition (EMD) denoising, and a hybrid EMD-wavelet denoising approach. The implementation involves generating noise signals using MATLAB's randn() function for white noise and raylrnd() function for Rayleigh-distributed noise. For wavelet denoising, we'll utilize wavelet transform functions like wavedec() and wden() with thresholding techniques. EMD denoising will employ the empirical mode decomposition algorithm to extract intrinsic mode functions (IMFs), while the hybrid method combines EMD's adaptive decomposition with wavelet's multi-resolution analysis. Through comparative analysis of these methods, we can better understand background noise characteristics in signals and develop more effective denoising strategies.

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