【matlab国外编程代写】GPS信号捕获matlab仿真

【matlab国外编程代写】GPS信号捕获matlab仿真
clear;
%指标是signalpower=-185dBW，此时可以进行室内定位；
%指标是SNR=-52dB，此时noisepower=-133dBW
Dopler_bound = 10; %原来是20，为加快运行速度，减少频率槽数
Freq_bin = 500;
Fc = 1250000;
Length_of_data = 5000;
Length_of_fft = 4096;
NUM = 5;%50010;
Signalpower = -177 %I路信号的功率（dBW）
Noisepower = -143 %噪声的复功率（dBW）
Threshold = 1e5;
Times_of_ncoh = 40;
%变量初始化
successcount=0;
%在卫星从地平线升起或降落时，用户速度900m/s时,径向速度差不多(+ -)10K，
fid_ca=fopen('CA_5000.txt','r');
CA_samp=fscanf(fid_ca,'%d');%5000点
fclose(fid_ca);
CA_samp=CA_samp';
%本地伪码FFT:CA_fft
CA_local=Average(Length_of_data, Length_of_fft, CA_samp);
%plot(CA_local)
CA_fft=fft(CA_local);
%plot(imag(CA_fft))
Idata=ReadIn( ); %把文本Idata全部读到Idata数组中
Qdata=ReadIn( ); %
Idata=Idata';
Qdata=Qdata';
Idata(1:NUM)=[]; %删去前NUM个元素
Qdata(1:NUM)=[];
AMP=sqrt(2*10^(Signalpower/10)) %输入量为幅度系数，单位相当于（V），%AMP=1e-9，此时signal power=-183dBW DIV_AWGN= 10^(Noisepower/10) %输入量为高斯白噪声的方差。

DIV_AWGN：在均值means 为0时，高斯白噪声的方差即噪声的功率单位为（W）
for
k=1:200 %%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%% %
%变量初始化
acq_tag = 0;
coh_count = 1;
ncoh_count = 1;
highdop_index = 1;%初始值
lowdop_index = 0;
%初始化矩阵并清零
R = zeros(20,(2*Dopler_bound+1)*Length_of_fft);
sum_cohe_accum = zeros(Times_of_ncoh,(2*Dopler_bound+1)*Length_of_fft);%相干累积和清零
sum_ncohe_accum = zeros(1,(2*Dopler_bound+1)*Length_of_fft);%非相干累积和清零ncohe_accum = zeros(1,(2*Dopler_bound+1)*Length_of_fft);
[gr,gi]=MyAwgn(62*5000*20-NUM,DIV_AWGN);%产生随机高斯白噪声：
Ich = Idata*AMP+gr;
Qch = Qdata*AMP+gi;
for n=1:Times_of_ncoh %把I和Q转变成二维数组Isn和Qsn Isn(n,1:1e5) = Ich(1+(n-1)*1e5 : n*1e5);
Qsn(n,1:1e5) = Qch(1+(n-1)*1e5 : n*1e5);
end;
for ncoh_count=1:Times_of_ncoh%%%进行非相干累积
while coh_count<=20 %%进行相干累积
while (highdop_index <= Dopler_bound) | (lowdop_index>=(-1)*Dopler_bound) %一次5000点搜索if lowdop_index >= Dopler_bound*(-1)
cos_localcarr = LocalCarrier(lowdop_index, Freq_bin, Fc);
sin_localcarr = LocalCarrier(lowdop_index, Freq_bin, Fc);
I=Isn(ncoh_count,1+(coh_count-1)*Length_of_data : coh_count*Length_of_data ).*cos_localcarr-Qsn(ncoh_count,1+(coh_count-1)*Length_of_data : coh_count*Length_of_data ).*sin_localcarr;
Q=Isn(ncoh_count,1+(coh_count-1)*Length_of_data : coh_count*Length_of_data ).*sin_localcarr+Qsn(ncoh_count,1+( coh_count-1)*Length_of_data : coh_count*Length_of_data ).*cos_localcarr;
I_short = Average( Length_of_data, Length_of_fft, I);
Q_short = Average( Length_of_data, Length_of_fft, Q);
Rev_fft = fft(I_short+i*Q_short); %接收来的信号做fft
R(coh_count,1+(lowdop_index+Dopler_bound)*Length_of_ff t : (lowdop_index+Dopler_bound+1)*Length_of_fft) = ifft(Rev_fft.*conj(CA_fft)); %相关值R，R其实是一个矩阵%
R_mod(coh_count,1+(lowdop_index+Dopler_bound)*Length_of_ fft : (lowdop_index+Dopler_bound+1)*Length_of_fft) = abs(R(coh_count,1+(lowdop_index+Dopler_bound)*Length_of_ff
t : (lowdop_index+Dopler_bound+1)*Length_of_fft));
end;
if highdop_index <= Dopler_bound
cos_localcarr = LocalCarrier(highdop_index, Freq_bin, Fc);
sin_localcarr = LocalCarrier(highdop_index, Freq_bin, Fc);
I=Isn(ncoh_count,1+(coh_count-1)*Length_of_data : coh_count*Length_of_data ).*cos_localcarr-Qsn(ncoh_count,1+(coh_count-1)*Length_of_data : coh_count*Length_of_data ).*sin_localcarr;
Q=Isn(ncoh_count,1+(coh_count-1)*Length_of_data : coh_count*Length_of_data ).*sin_localcarr+Qsn(ncoh_count,1+( coh_count-1)*Length_of_data : coh_count*Length_of_data ).*cos_localcarr;
I_short = Average( Length_of_data, Length_of_fft, I);
Q_short = Average( Length_of_data, Length_of_fft, Q);
Rev_fft = fft(I_short+i*Q_short); %接收来的信号做fft
R(coh_count,1+(highdop_index+Dopler_bound)*Length_of_ fft : (highdop_index+Dopler_bound+1)*Length_of_fft) = ifft(Rev_fft.*conj(CA_fft));
%
R_mod(coh_count,1+(highdop_index+Dopler_bound)*Length_of _fft : (highdop_index+Dopler_bound+1)*Length_of_fft) = abs(R(coh_count,1+(highdop_index+Dopler_bound)*Length_of_ fft : (highdop_index+Dopler_bound+1)*Length_of_fft));
end;
lowdop_index = lowdop_index-1;
highdop_index = highdop_index+1;
end; %结束时域搜索频率，求得单次相关值
%共进行2×Dopler_bound+1次，即R 实际是一个21×4096型矩阵
%再进行相干累积20次，即R 实际是一个20×21×4096型矩阵% [peak,locate]=max(R_mod(coh_count, : ));
% disp(Threshold);
% if peak>Threshold %门限值要科学慎重计算，调试，得到。

% acq_tag=1;
% freqbin_index = ceil(locate/Length_of_fft); %向上取整，求出行号即多普勒频槽号
% freqbin_index = freqbin_index-Dopler_bound-1;
% CA_phase = mod(locate,Length_of_fft); %取模，求出列号即码相位偏移号
% CA_phase = Unaverage(Length_of_fft, Length_of_data, CA_phase);
% CA_phase = CA_phase-1;
% disp(coh_count);
% disp(freqbin_index);
% disp(CA_phase);
% break;
% else
sum_cohe_accum(ncoh_count, : ) = sum_cohe_accum(ncoh_count, : ) + R(coh_count, : );
coh_count=coh_count+1;
% if coh_count == 2 & ncoh_count == 1
% for p=1:21
% picture(p,:) = R(coh_count, 1+(p-1)*4096 : 4096*p );
% end;
% mesh(picture);
% end;%把一次相关结果转化成picture二维矩阵，画三维图% end;
end; %结束20ms的20次相干累积
% if ncoh_count == 1
% for p=1:21
% picture(p,:) = sum_cohe_accum(1, 1+(p-1)*4096 : 4096*p );
% end;
% mesh(picture);
% end;%把20次相干累积结果转化成picture二维矩阵，画三维图
ncohe_accum( : ) = (abs(sum_cohe_accum(ncoh_count, : )/20)).^2;%%%%%%%%% %%%%%%%%%%%%%%%%%%% %%%%%
sum_ncohe_accum( : ) = sum_ncohe_accum( : ) + ncohe_accum( : );%求非相干累积和
%
end;%结束40次非相干累积
for p=1:21
picture(p, : ) = sum_ncohe_accum( 1+(p-1)*4096 : 4096*p );
end;
mesh(picture);
hold on;
[peak, locate] = max(sum_ncohe_accum);
freqbin_index = ceil(locate/Length_of_fft); %向上取整，求出行号即多普勒频槽号
freqbin_index = freqbin_index-Dopler_bound-1;%freqbin_index取值范围从-Dopler_bound，到+Dopler_bound CA_phase = mod(locate,Length_of_fft); %取模，求出列号即码相位偏移号
CA_phase = Unaverage(Length_of_fft, Length_of_data, CA_phase);
CA_phase = CA_phase-1;
disp(peak);
disp(freqbin_index);
disp(CA_phase);
if (freqbin_index==2) & (CA_phase==(Length_of_data-mod(NUM,Length_of_data))|CA_phase==(Length_of_data-
mod(N UM,Length_of_data)-1)|CA_phase==(Length_of_data-mod(NUM,Length_of_data)-2)|CA_phase= =(Length_of_data-mod(NUM,Length_of_data)+1))
successcount = successcount+1;
acq_tag = 1;
end;
end;
disp(successcount);
% fclose(fid1);%捕获结束，关掉文本指针
% fclose(fid2);%捕获结束，关掉文本指针
%将5000->4096点，验证得出此函数正确,n是原来长度，k是调整后长度function [numshort] = Average( n, k, numlong ) rate=n/k;
for m=1:k
count(m)=0;
sum(m)=0;
for l=1:n
if (floor((l-1)/rate))==(m-1)
count(m)=count(m)+1;
sum(m)=sum(m)+numlong(l);
end;
end;
numshort(m)=sum(m)/(count(m));
end;
function [ cos_localcarr sin_localcarr ] = LocalCarrier(fd_index, freq_bin, fc)
%本地载波
sam_rate=5000000;
sam_intervel=1.0/sam_rate;
fr=fc+fd_index*freq_bin;
k=linspace(0,4999,5000);
cos_localcarr=10*cos(2*pi*fr*sam_intervel*k);
sin_localcarr=10*sin(2*pi*fr*sam_intervel*k);
function [ g1,g2 ] = MyAwgn( n, div)
%****** complex Gauss white noise
%****** 均值为0
%n为向量维数
%div %复高斯白噪声的方差
%实高斯白噪声的方差为div/2
mean = 0;%meanan
u1 = rand(1,n);%在【0，1】上均匀分布的n个数
u2 = rand(1,n);
a = sqrt(-2.*log(u1));%由随机信号分析得到
b = 2*pi.*u2;
g1 = sqrt(div/2)*a.*cos(b)+mean;%实高斯白噪声，作为实部g2 = sqrt(div/2)*a.*sin(b)+mean;%实高斯白噪声，作为虚部function [ output_args ] = Untitled1( input_args )
p=D;N=50000;%N的大小由你实际情况而定
u=randn(1,N);a=sqrt(p);
u=u*a;
save u;。