MATLAB and C Programs - Digital Signal Processing: A Practitioner's Approach - page 141

p=fix(p1+p2);
x3=inv(A'*A)*A'*p;
ek=A*x3-p;
sigma=std(ek);
x=x_val-3:0.1:x_val+3;
for i=1:row_max
q=A(i,:);
y=-q(1)/q(2)*x+p(i)/q(2);
B(i,:)=x;C(i,:)=y;
end
N=length(x);
i=i+1;
x_h=linspace(min(min(B)),max(max(B)), N);
x_v=ones(size(x_h))*y_val;
y_v=linspace(min(min(C)),max(max(C)), N);
y_h=ones(size(y_v))*x_val;
theta=linspace(0,2*pi,N);
x=x3(1)-sigma*cos(theta);
y=x3(2)-sigma*sin(theta);
subplot(211);
plot(B',C','.',y_h,y_v,x_h,x_v,x,y,'o',x,y);grid;
% print -depsc f21_6
subplot(212)
stem(ek);grid
% print -depsc f21_7
A.3 Chapter 3 MATLAB Programs
This chapter described ways to specify a given filter. We presented a variety of
popular filters and their characteristics. We looked at IIR filters with real coeffi-
cients and truncated IIR filters for use in FIR designs. We also described a bank of
filters with complex coefficients pointing to discrete Fourier transforms. We gave a
simple presentation of adaptive filters and inverse problems having practical
significance. We demonstrated an application of BFSK demodulation and adaptive
phase shifting. And we looked at a practical inverse problem from target tracking.
We added an important dimension by giving Kalman filter applications. Then we
concluded this big topic with an application from array processing.
A.3.1 Program f 3_1
%Digital Signal Processing:A Practitioner's Approach
%Dr.Kaluri Venkata Ranga Rao , [e-mail]
%Generates figure 3.1