sa_ex7_7.m

智能天线Matlab版源代码

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%%   ******************************************************   %%
%%   * Smart Antennas for Wireless Applications w/ Matlab *   %%
%%   ******************************************************   %%
%%                                                            %%
%% Chapter 7: Ex 7.7                                          %%
%%                                                            %%
%% Author: Frank Gross                                        %%
%% McGraw-Hill, 2005                                          %%
%% Date:  1/26/2004                                           %%
%%                                                            %%
%% This code creates Figure 7.4, plot of a Linear Predictive  %%
%%     Pseudospectrum for theta1= -5 & theta2 = 5             %% 
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%%------------------- Define Variables:-----------------------%%
% M - # of elements in array                                   %
% sig2 - noise variance                                        %
% th1, th2 - angles of arrival                                 %
% a1, a2, a - array steering vectors                           %
% A - Steering vector matrix                                   %
% Rss - Source correlation matrix                              %
% Rxx - Array correlation matrix                               %
% P - Linear Predictive Psuedospectra                          %
%%------------------------------------------------------------%%

%%----- Given Values -----%%

M = 6;  sig2 = 0.1;

th1 = -5*pi/180;  th2 = 5*pi/180;

%%----- Create steering vectors, a1 & a2 -----%%

a1 = [];  a2 = [];  u3 = [0 0 1 0 0 0];
i = 1:M;
a1 = exp(1j*(i-1)*pi*sin(th1));
a2 = exp(1j*(i-1)*pi*sin(th2));

%%----- Create A, Rss, Rxx -----%%

A = [a1' a2'];  Rss = [1 0;0 1]; Rxx = A*Rss*A' + sig2*eye(M);

for k = 1:180;
    th(k) = -pi/6 + pi*k/(3*180);
    clear a
    a = [];
    for jj = 1:M
       a = [a exp(1j*(jj-1)*pi*sin(th(k)))];
    end
    P(k) = real((u3*inv(Rxx)*u3')/(abs(u3*inv(Rxx)*a.')^2)); 
end

%%----- Plot Results -----%%

plot(th*180/pi,10*log10(P/max(P)),'k')
title(['\bfFigure 7.4 - Linear Predictive Psuedospectra for \theta = +/- 5^o'])
xlabel('Angle'), ylabel('|P(\theta)|')
axis([-30 30 -30 10]), grid on