output.m

GALILEO BOC信号自相关函数特性分析

%boc(2,2)本地副载波 即时/ 前移 n/16形成的本地boc 与接收boc(未经移位)之间的互相关值比较
close all
clear all
clc

FZ1 = [ones(1,8) -1*(ones(1,8))];%未经移位的副载波
FZ2=[FZ1(2:length(FZ1)) FZ1(1)];%前移位1/16的副载波
FZ3=[FZ1(3:length(FZ1)) FZ1(1:2)];%前移位2/16的副载波
FZ4=[FZ1(4:length(FZ1)) FZ1(1:3)];%前移位3/16的副载波
FZ5=[FZ1(5:length(FZ1)) FZ1(1:4)];%前移位4/16的副载波

PN=createpn;
sample_rate=1;%采样率

Buffer1 = []; 
for n = 1:length(PN)
            TxDataBin = PN(n:n);%将PN序列打散成单个码片进行调制
            TxSymbol = Mapping(TxDataBin,FZ1); 
            TxSignal = Modulate(TxSymbol,FZ1,sample_rate); 
            Buffer1 = [Buffer1 TxSignal];
end
BOC1=Buffer1;

Buffer2 = []; 
for n = 1:length(PN)
            TxDataBin = PN(n:n);
            TxSymbol = Mapping(TxDataBin,FZ2); 
            TxSignal = Modulate(TxSymbol,FZ2,sample_rate); 
            Buffer2 = [Buffer2 TxSignal];
end
BOC2=Buffer2;

Buffer3 = []; 
for n = 1:length(PN)
            TxDataBin = PN(n:n);
            TxSymbol = Mapping(TxDataBin,FZ3); 
            TxSignal = Modulate(TxSymbol,FZ3,sample_rate); 
            Buffer3 = [Buffer3 TxSignal];
end
BOC3=Buffer3;

Buffer4 = []; 
for n = 1:length(PN)
            TxDataBin = PN(n:n);
            TxSymbol = Mapping(TxDataBin,FZ4); 
            TxSignal = Modulate(TxSymbol,FZ4,sample_rate); 
            Buffer4 = [Buffer4 TxSignal];
end
BOC4=Buffer4;

Buffer5 = []; 
for n = 1:length(PN)
            TxDataBin = PN(n:n);
            TxSymbol = Mapping(TxDataBin,FZ5); 
            TxSignal = Modulate(TxSymbol,FZ5,sample_rate); 
            Buffer5 = [Buffer5 TxSignal];
end
BOC5=Buffer5;


%一张图像表示，用线型区分
figure
[pxg,plags]=xcorr(BOC1,BOC1);
[q1xg,q1lags]=xcorr(BOC1,BOC2);%接收的boc信号与本地前移1/16位boc信号作互相关
[q2xg,q2lags]=xcorr(BOC1,BOC3);%接收的boc信号与本地前移2/16位boc信号作互相关
[q3xg,q3lags]=xcorr(BOC1,BOC4);%接收的boc信号与本地前移3/16位boc信号作互相关
[q4xg,q4lags]=xcorr(BOC1,BOC5);%接收的boc信号与本地前移2/16位boc信号作互相关
t=plags./(sample_rate*16);
plot(t,pxg/max(pxg),'-',t,q1xg/max(q1xg),':',t,q2xg/max(q2xg),'-.',t,q3xg/max(q3xg),'--',t,q4xg/max(q4xg),'.-','LineWidth',2);
legend('prompt','early 1/16','early 2/16','early 3/16','early 4/16');
title('BOC(2,2)相关')
ylabel('归一化相关值R')
axis([-2 2 -1 1]);
grid on