www.pudn.com > cfar.rar > ex9_14.m

clear all; 
close all; 
clc; 
%%%%%%%%%%%产生瑞利噪声%%%%%%%%%%%%%%%%%%%%%%% 
sigma=2; 
t=1e-3; 
fs=1e6; 
ts=1/fs; 
t1=0.05e-3:1/fs:0.2e-3-1/fs; 
n=length(t1); 
rand('state',0); 
u=rand(1,n); 
rayleigh_noise=sqrt(2*log2(1./u))*sigma; 
 
%%%%%%%%%%%%产生目标回波%%%%%%%%%%%%%%%%%%% 
N=t/ts; 
s_pc_1=[zeros(1,100),1,zeros(1,N-101)]; 
noise=rand(1,N); 
rayleigh_clutter=[zeros(1,50),rayleigh_noise,zeros(1,N-200)]; 
s_pc=s_pc_1+0.1*rayleigh_clutter+0.1*noise; 
figure,plot((0:ts:t-ts),s_pc), 
xlabel('t(单位:s)'),title('叠加了瑞利分布杂波、热噪声的目标回波'); 
 
%%%%%%%%%%%%%慢门限恒虚警处理%%%%%%%%%%%%%%%%%%% 
 cfar_result=zeros(1,N); 
 cfar_result(1,1)=s_pc(1,1); 
 for i=2:N 
     cfar_result(i)=s_pc(1,i)/mean(s_pc(1,1:i)); 
 end 
 figure,plot((0:ts:t-ts),cfar_result), 
xlabel('t(单位:s)'),title('采用慢门限处理结果'); 
 
%%%%%%%%%%%%%%%快门限恒虚警处理%%%%%%%%%%%%% 
cfar_k_result=zeros(1,N); 
 
cfar_k_result(1,1)=s_pc(1,1)/(sqrt(2)/pi*mean(s_pc(1,2:17))); 
  
%第l点恒虚警处理时噪声均值由其后面的l6点的噪声决定 
for i=2:16 
%第2点到第16点的恒虚警处理的噪声均但由其前面和后面l6点的噪声共同决定 
    noise_mean=sqrt(2)/pi*(mean(s_pc(1,1:i-1))+mean(s_pc(1,i+1:i+16)))/2; 
    cfar_k_result(1,i)=s_pc(1,i)/noise_mean; 
end 
for i=17:N-17 
%正常的数据点的恒虚警处理的噪声均值由其前面和后面各26点的噪声中的决定 
    noise_mean=sqrt(2)/pi*max(mean(s_pc(1,i-16:i-1)),mean(s_pc(1,i+1:i+16))); 
    cfar_k_result(1,i)=s_pc(1,i)/noise_mean; 
end 
for i=N-16:N-1 
%例数第l6点到倒数第2点恒虚警处理的噪声均值由其前面16和后面的噪声共同决定 
    noise_mean=sqrt(2)/pi*(mean(s_pc(1,i-16:i-1))+mean(s_pc(1,i+1:N)))/2; 
    cfar_k_result(1,i)=s_pc(1,i)/noise_mean; 
end 
%最后-点的桓虚警处理的噪声均值由其前面l6点的噪声决定 
cfar_k_result(1,N)=s_pc(1,N)/(sqrt(2)/pi*mean(s_pc(1,N-16:N-1))); 
figure,plot(0:ts:t-ts,cfar_k_result),xlabel('t(单位:s)'),title('采用快门限恒虚警处理处理结果');