www.gusucode.com > signal 案例源码程序 matlab代码 > signal/ReflectionCoefficientsForModelOrderDeterminationExample.m
%% Reflection Coefficients for Model Order Determination % Create a realization of an AR(4) process. Use |arburg| to determine the % reflection coefficients. Use the reflection coefficients to determine an % appropriate AR model order for the process. Obtain an estimate of the % process PSD. % Copyright 2015 The MathWorks, Inc. %% % Create a realization of an AR(4) process 1000 samples in length. Use % |arburg| with the order set to 12 to return the reflection coefficients. % Plot the reflection coefficients to determine an appropriate model order. A = [1 -2.7607 3.8106 -2.6535 0.9238]; rng default x = filter(1,A,randn(1000,1)); [a,e,k] = arburg(x,12); stem(k,'filled') title('Reflection Coefficients') xlabel('Model Order') %% % The reflection coefficients decay to zero after order 4. This indicates % an AR(4) model is most appropriate. % % Obtain a PSD estimate of the random process using Burg's method. Use 1000 % points in the DFT. Plot the PSD estimate. pburg(x,4,length(x))