基于cyclic-MUSIC的DOA估计Matlab仿真

1、信息与通信工程学院 阵列信号处理实验报告 （基于cyclic-MUSIC的DOA估计Matlab仿真）学 号：XXXXXX专 业：XXXXXX学生姓名：XXX任课教师：XXX2015年X月题目：基于cyclic-MUSIC的DOA估计Matlab仿真1. 算法简述：DOA估计算法一般使用与平稳信号，而生活中测量的信号往往是非平稳的。在非平稳的信号中，有一类信号属于周期平稳信号，利用周期平稳性得到的算法称循环（共轭循环）平稳算法。循环平稳的循环自相关函数表示为其中为循环角频率。共轭循环平稳的循环自相关函数表示为 基于天线阵列协方差矩阵的特征分类DOA估计算法中，MUSIC算法具有普遍的实用性。只

2、要已知天线阵的分布形式，无论直线阵还是圆阵，不管阵元是否等间隔分布，都可以得到高分辨率的估计结果。2. 实验内容与结果：实验先仿真分析BPSK信号的循环平稳和共轭循环平稳性与循环频率和延时的的关系，然后使用12阵元均匀线阵，阵元间距为信号波长的一半，输入信号为1个BPSK信号、设置载波频率10MHz、采样频率50MHz、快拍数1000、信号方位角-4、信噪比为25dB情况下，得到DOA估计结果。图1 BPSK 的循环平稳性图2 BPSK共轭循环平稳性图3 DOA仿真结果3. 仿真分析由仿真结果可知，BPSK信号具有循环平稳特性，当循环频率为，信号具有循环平稳性，当循环频率为，信号具有共轭循环平

3、稳性。由图一图二可得到合适的循环频率和延时，例如图一，循环频率为、延时为40Ts时其循环自相关函数值最大。实验最后针对cyclic-music算法的DOA估计，使用共轭循环平稳性设计算法，并在给定实验条件下仿真，有效估计出了信号的来向。4. 程序1. 程序1：clear all;close all;clc;%-%线阵 波束形成%-%参数设置S_No = 1;sensor_No = 12;azimuth = 40*pi/180 ;Fs = 40e6;Fc = 10e6; %干扰功率RB = 1e6;M = 2;%二进制wavelength = 3e8/Fc;d = wavelength/2;K

4、= 1000; %快拍数%-%dBPs = -0;SNR = 20;M_No = 40; %码速率等于信息速率Data_No = M_No*Fs/RB;t = 1/Fs:1/Fs:Data_No/Fs;%-%功率转换Ps_l = 10(Ps/10);Pn_l = 10(Ps-SNR)/10);%-%信号生成bit = randint(1,M_No); %产生信息序列bitstream = ;for i = 1:M_No if bit(i) = 1 bitstream = bitstream, ones(1, Fs/RB); else bitstream = bitstream, -ones(1

5、, Fs/RB); endendCarrier_R = cos(2*pi*Fc*t);S_R = Carrier_R .* bitstream;Carrier_I = sin(2*pi*Fc*t);S_I = Carrier_I .* bitstream;Signal_R = sqrt(Ps_l)*S_R/sqrt(S_R*S_R/length(S_R);Signal_I = sqrt(Ps_l)*S_I/sqrt(S_I*S_I/length(S_I);%生成BPSK复信号Signal(1,:) = complex(Signal_R, Signal_I);%-%-%模拟天线接收As = ze

6、ros(sensor_No,S_No);for i = 1:sensor_No for ii = 1:S_No As(i,ii) = exp(-1i*2*pi*sin(azimuth(ii)*d/wavelength*(i-1); endendx = As * Signal;%-%加噪声noise_R = randn(sensor_No,Data_No)*sqrt(Pn_l);noise_I = randn(sensor_No,Data_No)*sqrt(Pn_l);noise = complex(noise_R, noise_I);s = x + noise;%X = Signal(1,(1

7、:K);Nm = length(X(1,:); % 产生共轭循环矩阵 flagg=1; Num1= 100;Num2=5;for n = 1:Num1 for Km = 0:39 Fcj=(n-Num1/2)/Num2*1e6;%选择循环频率 x1=X(1:Nm-Km)-mean(X(1:Nm-Km); x2=(X(1+Km:Nm)-mean(X(1+Km:Nm).*exp(-1i*2*pi*Fcj/Fs*(1:Nm-Km); if flagg=1 xx=x1*x2; %循环平稳算法 xn=xx/(Nm-Km);elseif flagg=2 xx=x1*conj(x2); %共轭循环平算法 x

8、n=xx/(Nm-Km);endphas=exp(1i*2*pi*Fcj/Fs*Km); R(n,Km+1)=xn*phas; endend% Max=0;% for m1=1:Num1% for m2=1:50% if R(m1,m2)=Max;% m3=m1;m4=m2;% Max=R(m1,m2);% end% end% end% m3=m3-Num1/2% m4aaa=-(Num1/2-1)/Num2:1/Num2:Num1/2/Num2;bbb=1:2:80;Rr=abs(R);meshc(bbb,aaa,Rr);ylabel(循环频率);xlabel(延时);zlabel(循环频率

9、模);2. 程序2：clear all;close all;clc;%-%线阵 music DOA%-%参数设置Signal_No = 1;Interference_No = 1;S_No = Signal_No + Interference_No;sensor_No = 8;azimuth = 0*pi/180 5*pi/180;Fs = 30e6;Fc = 10e6;F1 = 8e6; %干扰功率theta = 10 45 52 93 43*pi/180;RB = 2e6;M = 2;%二进制wavelength = 3e8/Fc;d = wavelength/2;K = 300; %快拍

10、数%-%dBPs = -30;SNR = 10;SIR = 00;M_No = 40; %码速率等于信息速率Data_No = M_No*Fs/RB;t = 1/Fs:1/Fs:Data_No/Fs;%-% for xunhuan=1:1% mean1 = 0;% mean2 = 0;% for SNR =1:20%-%功率转换Ps_l = 10(Ps/10);Pi_l = 10(Ps-SIR)/10);Pn_l = 10(Ps-SNR)/10);%-%信号生成bit = randint(1,M_No); %产生信息序列bitstream = ;for i = 1:M_No if bit(i)

11、 = 1 bitstream = bitstream, ones(1, Fs/RB); else bitstream = bitstream, -ones(1, Fs/RB); endendCarrier_R = cos(2*pi*Fc*t);S_R = Carrier_R .* bitstream;Carrier_I = sin(2*pi*Fc*t);S_I = Carrier_I .* bitstream;Signal_R = sqrt(Ps_l)*S_R/sqrt(S_R*S_R/length(S_R);Signal_I = sqrt(Ps_l)*S_I/sqrt(S_I*S_I/len

12、gth(S_I);%生成BPSK复信号Signal(1,:) = complex(Signal_R, Signal_I);%-%产生干扰%I_R = sqrt(2*Pi_l)*cos(2*pi*F1*t);%I_I = sqrt(2*Pi_l)*sin(2*pi*F1*t);for i = 2:Interference_No + 1 Signal(i,:) = sqrt(2*Pi_l)*complex(sin(2*pi*F1(i-1)*t+theta(i), sin(2*pi*F1(i-1)*t+theta(i);end%-%模拟天线接收As = zeros(sensor_No,S_No);f

13、or i = 1:sensor_No for ii = 1:S_No As(i,ii) = exp(-1i*2*pi*sin(azimuth(ii)*d/wavelength*(i-1); endendx = As * Signal;%-%加噪声noise_R = randn(sensor_No,Data_No)*sqrt(Pn_l);noise_I = randn(sensor_No,Data_No)*sqrt(Pn_l);noise = complex(noise_R, noise_I);s = x + noise;x1 = s(:,(1:K);R=x1*x1/K;V,D=eig(R);D

14、1=diag(D);Un=V(:,1:sensor_No-S_No);Gn=Un*Un;searching_doa=-90:0.1:90;%线阵的搜索范围为-9090度 for i=1:length(searching_doa) a_theta=exp(-j*(0:sensor_No-1)*2*pi*d*sin(pi*searching_doa(i)/180)/wavelength); Pmusic(i)=1./abs(a_theta)*Gn*a_theta); endplot(searching_doa,10*log(Pmusic),r);xlabel(入射角/degree);ylabel(

15、空间谱/dB);legend(Music Spectrum);title(经典MUSIC估计);grid on;%-% 查最大值 %-% Pmax_db1=0;% for i=1:909% if Pmusic(i)Pmax_db1% Pmax_db1=Pmusic(i);% Pmax_pitching1=i;% end% end% Pmax_db2=0;% for i=910:1801% if Pmusic(i)Pmax_db2% Pmax_db2=Pmusic(i);% Pmax_pitching2=i;% end% end% err1(SNR) = (Pmax_pitching1-1)/10-90)2;% err2(SNR) = (Pmax_pitching2-1)/10-90-5)2;% end% mean1 = mean1 + err1;% mean2 =