Explore MATLAB source code curated for "白噪声" with clean implementations, documentation, and examples.
Estimating the frequency of complex sinusoidal signals contaminated by white noise using ESPRIT algorithm, where parameter f provides the frequency estimation value for the sinusoidal component
MATLAB simulation diagrams of the Duffing chaotic oscillator system demonstrating weak signal detection under noise-free, white noise, and colored noise conditions through parameter selection and signal processing techniques, including implementation approaches for chaotic system modeling and noise filtration algorithms.
This MATLAB-based virtual signal generator source code enables generation of arbitrary waveforms including sine, square, triangular waves, and white noise with configurable frequency, amplitude, and duration parameters.
Custom MATLAB source code for LMS adaptive algorithm implementation. This program generates a signal combined with white noise sequence and utilizes the LMS algorithm for adaptive noise cancellation. lmsx.m serves as the adaptive filter function, while task2.m acts as the main function that calls lmsx.m.
Generate a uniformly distributed white noise signal with zero mean and power p, plot its waveform, and validate its distribution. Additionally, generate zero-mean Gaussian white noise with power 0.1, sinc signals, chirp signals, and demonstrate linear convolution operations with implementation details.
This MATLAB-based implementation demonstrates OFDM system simulation under additive white Gaussian noise (AWGN) and Rayleigh fading channel conditions, featuring comprehensive performance analysis and detailed code annotations.
Implementation of spectral analysis for sine waves, rectangular signals, and white noise using MATLAB's FFT function with code-based parameter configuration and algorithm explanations
Application Background Power spectrum, short for power spectral density function, is defined as signal power per unit frequency band. It indicates how signal power changes with frequency, representing the distribution of signal power in the frequency domain. Key Technologies Power spectral density provides a power description method distinct from time-domain analysis, commonly used in statistical signal processing. Two fundamental applications are introduced: (1) Definition of white noise and colored noise. If a signal's power spectrum equals a constant, the random process is called white noise; otherwise, it's termed colored noise. (2) Calculating signal's autocorrelation function using its relationship with autocovariance function.
Custom 2ASK simulation program developed without MATLAB's built-in functions, incorporating white noise addition and filtering. The filtered waveform shows some distortion, potentially due to filter characteristics. This serves as a reference implementation demonstrating manual signal processing techniques.
ROC performance curve visualization for energy detection algorithm in Additive White Gaussian Noise (AWGN) channel with implementation insights.