Matlab Code for Monson H. Hayes Statistical DSP Computer Exercise C3.4

Matlab Code for Monson H. Hayes Statistical DSP Computer Exercise C3.4 of chapter 3. The problem, matlab code and output of simulation is shown below.

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% Matlab code for the above problem
clc;
clear all; 
close all;
a(1) = 0;
a(2) = -0.81;
b(1) = 1;       % b(0) = 1 for the purpose of MATLAB indexing
x(1) = 1;       % x(0) = 1
x(2) = 0;       % x(1) = 2
x(3) = 0.81;    % x(2) = 0.81

for n= 4:24
    x(n) = a(1) * x(n-1) + a(2) * x(n-2);
end

CoefM = [1, a(1), a(2); a(1), 1, a(1); a(2), a(1), 1];  %Coefficient matrix
C0 = [1;0;0];
rx = inv(CoefM) * C0;   % rx(1), rx(2), rx(3)

for k = 4:24
    rx(k) = -a(1)*rx(k-1) - a(2)*rx(k-2);   % [rx(4), rx(24)]
end

rxHat = autocorr(x,23);      % Estimated Autocorrelation

px = fft(rx);                         % True PSD
Pxt=abs(px);                          % Magnitude of Estimated power

Pt = fft(rxHat);                      % Estimated PSD
Pxc = abs(Pt);                        % Magnitude of Estimated PSD

d = [rx(1), rx(2); rx(2), rx(1)];
s = -[rx(2); rx(3)];
g = inv(d) * s;                       % Computing a(1) and a(2) using Yule-Walker Equation
b0 = rx(1) + g(1)*rx(2) + g(2)*rx(3); % Computing b(0) using Yule-Walker Equation

syms w
pxe = b0^2/(abs(1+g(1)*exp(-1i*w) + g(2)*exp(-1i*2*w)).^2);

disp('Coefficients found Using Yule-Walker Equations:');
disp('===============================================');
disp('a(1):'),disp(g(1));
disp('a(2):'),disp(g(2));
disp('b(0):'),disp(b0);

subplot(3,2,1), stem(x);
title('x(n)')
subplot(3,2,3), stem(rx);
title('True Autocorrelation')
subplot(3,2,5),autocorr(x,'NumLags',23);
title('Estimated Autocorrelation')
subplot(3,2,2),plot(Pxc);
title('Estimated Power Spectrum  part c')
subplot(3,2,4),fplot(pxe, [-2*pi, 2*pi]);
title('Estimated Power Spectrum  part e')
subplot(3,2,6),plot(Pxt);
title('True Power Spectrum');
Matlab Code Output for Monson H. Hayes Statistical DSP Problems C3.4
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