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))