Radar Detection - MATLAB Simulations for Radar Systems Design - page 119

hold off
xlabel ('Single pulse SNR - dB')
ylabel ('Probability of detection')
grid
Listing 2.6. MATLAB program Ðfig2_6a.mÑ
% This program is used to produce Fig. 2.6a
% It uses the function "improv_fac"
clear all
close all
pfa1 = 1.0e-2;
pfa2 = 1.0e-6;
pfa3 = 1.0e-10;
pfa4 = 1.0e-13;
pd1 = .5;
pd2 = .8;
pd3 = .95;
pd4 = .999;
index = 0;
for np = 1:1:1000
index = index + 1;
I1(index) = improv_fac (np, pfa1, pd1);
I2(index) = improv_fac (np, pfa2, pd2);
I3(index) = improv_fac (np, pfa3, pd3);
I4(index) = improv_fac (np, pfa4, pd4);
end
np = 1:1:1000;
semilogx (np, I1, 'k', np, I2, 'k--', np, I3, 'k-.', np, I4, 'k:')
xlabel ('Number of pulses');
ylabel ('Improvement factor I - dB')
legend ('pd=.5, nfa=e+2','pd=.8, nfa=e+6','pd=.95, nfa=e+10','pd=.999,
nfa=e+13');
grid
Listing 2.7. MATLAB Function Ðimprov_fac.mÑ
function impr_of_np = improv_fac (np, pfa, pd)
% This function computes the non-coherent integration improvement
% factor using the empirical formula defined in Eq. (2.49)
fact1 = 1.0 + log10( 1.0 / pfa) / 46.6;
fact2 = 6.79 * (1.0 + 0.235 * pd);
fact3 = 1.0 - 0.14 * log10(np) + 0.0183 * (log10(np))^2;