Explore MATLAB source code curated for "噪声" with clean implementations, documentation, and examples.
MATLAB code implementation for target tracking simulation, including measurement generation, noise and clutter modeling, as well as various target motion dynamics with detailed algorithmic explanations
MATLAB-based moving target detection program designed for radar systems to extract targets from noise interference, featuring signal processing algorithms and clutter suppression techniques.
Improved region-based image segmentation algorithm particularly suitable for noisy images! Successfully compiled and tested in MATLAB 6.5 environment
Speech pre-emphasis and de-emphasis techniques for preliminary voice processing and noise reduction, with code implementation considerations.
This dataset contains electrocardiogram (ECG) signals with inherent noise, suitable for developing and testing medical signal processing algorithms to analyze cardiac conditions and improve diagnostic accuracy.
Smallest-of Constant False Alarm Rate Detection - SO-CFAR Detector: Includes simulations of SO-CFAR detection for target-free signals with noise only, as well as CA algorithm simulations for target-containing signals in Rayleigh clutter background. Some programs are designed for plotting to visualize signal waveforms and detection threshold comparisons during SO-CFAR detection process; others are developed to calculate detection probability and false alarm probability statistics.
This article presents the design of an FIR bandpass filter using the rectangular window method to eliminate noise from music signals, with implementation details including filter coefficient calculation and frequency response analysis.
Image denoising represents an essential aspect of image processing, constantly requiring a balance between noise suppression and detail preservation. Wavelet transform has emerged as a powerful signal processing tool with successful applications in noise removal. Traditional orthogonal wavelet-based denoising methods often cause oscillations near edges leading to edge distortion, and can produce blurred edges under severe noise conditions. Redundant wavelet transform techniques overcome these limitations and significantly enhance denoising performance through improved signal representation.
This compact program solves Duffing equations, analyzes chaotic states through phase portraits, and evaluates signal processing with noise injection capabilities
Simulation and Performance Analysis of the MUSIC Algorithm: Number of sources N=3, with angles of arrival at -10 degrees, 0 degrees, and 10 degrees; sources are mutually independent with equal amplitudes; noise is independent and follows a Gaussian distribution; uniform linear array with 8 elements and wavelength λ.