正弦信号 Resources

Showing items tagged with "正弦信号"

Estimating the frequency of a sinusoidal signal contaminated with additive white Gaussian noise via FFT involves computing the Fourier transform of x(n) to obtain the spectrum, identifying the frequency corresponding to the maximum magnitude, and calculating the mean squared error over multiple iterations. By varying the signal-to-noise ratio (SNR), simulations demonstrate that the mean squared error decreases as SNR increases, highlighting the method's robustness in noisy environments.

MATLAB 225 views Tagged
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Generate a random signal and two sinusoidal signals with different but closely-spaced frequencies, then perform comprehensive signal analysis including: (1) calculating autocorrelation coefficients and correlation functions with corresponding plots; (2) computing power spectra using different parametric modeling methods; (3) estimating parameters for AR, MA, and ARMA models using maximum likelihood estimation and recursive least squares, with comparison to MATLAB toolbox functions; (4) spectral estimation using notch filtering and MUSIC methods; (5) noise reduction using Wiener and LMS filtering

MATLAB 237 views Tagged
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This project implements frequency estimation of sinusoidal signals embedded in Gaussian white noise through three high-resolution spectral estimation methods: Pisarenko Harmonic Decomposition, MUSIC (Multiple Signal Classification) algorithm, and ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) algorithm. The sinusoidal signal is defined with specific frequency components, while the additive white Gaussian noise has controlled variance. Using 128 data samples, the implementation involves: 1) Performing 20 independent trials with each algorithm to record frequency estimates and compute statistical mean and variance; 2) Analyzing algorithm performance under increasing noise power conditions to evaluate robustness and accuracy.

MATLAB 497 views Tagged
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