Explore MATLAB source code curated for "随机信号" with clean implementations, documentation, and examples.
Implementation of fast approximate entropy algorithm for time series complexity analysis, capable of processing both deterministic and random signals with significantly improved computational efficiency
This article demonstrates the implementation of random signal simulation for user data streams, featuring valuable code examples and algorithmic approaches worth exploring
Implementation of approximate entropy algorithm for time series complexity analysis, capable of distinguishing deterministic signals from random signals through pattern regularity assessment
MATLAB-based spectral analysis of random signals in the frequency domain, covering power spectrum, cross-spectrum, window functions, and practical implementation techniques.
Program for generating ASK, PSK, and FSK random signals with code-based implementation approaches and practical modulation techniques.
Implementation of power spectrum estimation for random signals through autocorrelation function and periodogram methods, with analysis of how data length, autocorrelation sequence length, signal-to-noise ratio, window functions, and averaging次数 affect spectral resolution, stability, main lobe width, and side lobe effects. Includes code implementation considerations for parameter optimization.
This program performs bit error rate analysis across a range of SNR values. For each SNR level, it implements: random signal generation with QPSK modulation, Alamouti space-time block coding, transmission through Rayleigh fading channels with AWGN addition, maximum ratio combining at the receiver, and maximum likelihood detection to calculate symbol error rates. The implementation demonstrates that a symbol error rate of 10^-3 corresponds to approximately 12-13 dB SNR, validating the Alamouti scheme's performance in MIMO systems.
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 implementation for generating random signals, calculating correlation functions, and estimating power spectral density with code-level explanations
Classic textbook "Introduction to Random Signals and Applied Kalman Filtering, 4th Edition" (2012 latest version) featuring comprehensive companion code implementations for practical learning