www.gusucode.com > signal 案例源码程序 matlab代码 > signal/CovarianceMethodPSDEstimateOfAR4ProcessExample.m
%% Covariance-Method PSD Estimate of an AR(4) Process % Create a realization of an AR(4) wide-sense stationary random process. % Estimate the PSD using the covariance method. Compare the PSD estimate % based on a single realization to the true PSD of the random process. % Copyright 2015 The MathWorks, Inc. %% % Create an AR(4) system function. Obtain the frequency response and plot % the PSD of the system. A = [1 -2.7607 3.8106 -2.6535 0.9238]; [H,F] = freqz(1,A,[],1); plot(F,20*log10(abs(H))) xlabel('Frequency (Hz)') ylabel('PSD (dB/Hz)') %% % Create a realization of the AR(4) random process. Set the random number % generator to the default settings for reproducible results. The % realization is 1000 samples in length. Assume a sampling frequency of 1 % Hz. Use |pcov| to estimate the PSD for a 4th-order process. Compare the % PSD estimate with the true PSD. rng default x = randn(1000,1); y = filter(1,A,x); [Pxx,F] = pcov(y,4,1024,1); hold on plot(F,10*log10(Pxx)) legend('True Power Spectral Density','pcov PSD Estimate')