MATLAB Examples

Example 8.6.5. A Comparison of Frequency Estimation Methods.

Contents

Case II: Multiple Signal Classification (MUSIC).

Workspace Initialization.

clc; clear; close all;

Signal Definition

N = [64 128 256 512]; % Number of samples used for each case.

NumOfRuns = 10;    % Number of frequency estimation functions to be calculated for every different data record.

% Open a new figure:
h1 = figure('NumberTitle', 'off','Name', ...
               'Figure 8.37 Frequency Estimation for a Process Consisting of 4 Complex Exponentials in WGN', ...
               'Visible','off','Position', [100 0 800 950]);

sigmaw = sqrt(0.5);
% Create frequency axis:
L = 1024;
w = 0:2*pi/L:2*pi*(1-1/L);

Pmusic = zeros(NumOfRuns,L);

for m=1:length(N)
    for k=1:NumOfRuns
           noise = sigmaw*randn(1,N(m));
                   n = 0:N(m)-1;
                phi = 2*pi*(rand(4,1) - 0.5);
       omegas = [0.2*pi 0.3*pi 0.8*pi 1.2*pi].';

        % Compute N(m) samples of the given Harmonic Process:
        x = sum(exp(1i*omegas*n + phi*ones(1,N(m)))) + noise;

Estimate the Power Spectrum using the MUSIC algorithm:

        p = 4;   % Number of complex exponentials present assumed known.
        M = N(m); % Size of autocorrelation matrix to be used in the following methods:
        Pmusic(k,:) = music(x,p,M).';
    end

    subplot(4,1,m)
    plot(w/pi,Pmusic,'k','LineWidth',0.1)
    title(['Overlay plot of ',num2str(NumOfRuns),' MUSIC Spectra Using ',num2str(N(m)),' samples'])
    grid on;
    axis tight;
    ylim([-45 10])

end

xlabel('Frequency (units of pi)');

% Restore the visibility of the figure.
set(h1, 'Visible','on');