signal_noise_real.m

三种DOA方法：经典的MUSIC,前后向空间平滑和改进的空间平滑算法

   %产生实余弦信号signal与高斯噪声noise的函数
   function   [signal_real,noise_gauss_complex]=signal_noise_real(antenna_number,signal_number,samplepoint_number,SNR,signal_frequency,sample_frequency)
             %生成高斯噪声
               sigma_noise=1;
               noise=normrnd(0,sigma_noise,antenna_number,samplepoint_number);
            %产生复正弦连续波信号signal
              signal_amplitude=sigma_noise*10.^(SNR/20);
% 采用两个正弦信号源，    s1(t)=A1*sin(2*pi*f0*t+Φ1)    f0=6GHz,Φ1=6?
%                         s2(t)=A2*sin(2*pi*f0*t+Φ2)
% 取相关系数时，Φ1保持不变，使Φ2变化
%                            ρ＝【0      0.1    0.2    0.3    0.4    0.5    0.6   0.7    0.8    0.9    0.95   0.96   0.97  0.98   0.99   1】
% 与Φ1相对应的Φ2的值变化为Φ2＝【95.8   90.1   84.3   78.3   72.2   65.8   59    51.4   42.7   31.8   24.1   22.2   20    17.5   14.1   6】
%  采用两个余弦信号源，   s1(t)=A1*cos(2*pi*f0*t+Φ1) f0=6GHz,Φ1=6?
%                         s2(t)=A2*cos(2*pi*f0*t+Φ2)
%  取相关系数时，Φ1保持不变，使Φ2变化
%                            ρ＝【0   0.1   0.2   0.3   0.4   0.5   0.6   0.7   0.8  0.9   0.95  0.96  0.97  0.98  0.99  1】
% 与Φ1相对应的Φ2的值变化为Φ2＝【96  90.5  84.7  78.7  72.7  66.2  59.3  51.7  43   31.9  24.2  22.3  20    17.5  14    6】
           signal_initialphase=[6   186    95.8      -174    42.7]*pi/180;
           for    row_2=1:signal_number;           
                  for column_2=1:samplepoint_number;
                      signal_phase(row_2)=2*pi*signal_frequency*column_2/sample_frequency+signal_initialphase(row_2);
                                signal_complex(row_2,column_2)=signal_amplitude(row_2)*exp(j*signal_phase(row_2));
                      signal_real(row_2,column_2)=sqrt(2)*signal_amplitude(row_2)*imag(exp(j*signal_phase(row_2)));
                 end;
          end;
    
%    %产生复高斯信号源signal与高斯噪声noise的函数
%                %生成复高斯型信号源
%                sigma_signal=sigma_noise*10.^(SNR/20);
%                for  row_signal=1:signal_number
%                     for column_signal=1:samplepoint_number;
%                          p1=rand(1,1);
%                          p2=rand(1,1);
%                          signal_gauss_real(row_signal,column_signal)=sqrt(-2*sigma_signal(row_signal)^2*log(p1))*cos(2*pi*p2);
%                          signal_gauss_imag(row_signal,column_signal)=sqrt(-2*sigma_signal(row_signal)^2*log(p1))*sin(2*pi*p2);
%                          signal_gauss(row_signal,column_signal)=signal_gauss_real(row_signal,column_signal)+j*signal_gauss_imag(row_signal,column_signal);
%                     end 
%                end
   %产生复高斯噪声noise的函数
          noise=normrnd(0,sigma_noise,antenna_number,samplepoint_number);
               %生成复高斯型噪声
               for   noise_row=1:antenna_number
                     for noise_column=1:samplepoint_number;
                         p1=rand(1,1);
                         p2=rand(1,1);
                         noise_gauss_real(noise_row,noise_column)=sigma_noise*sqrt(-2*log(p1))*cos(2*pi*p2);
                         noise_gauss_imag(noise_row,noise_column)=sigma_noise*sqrt(-2*log(p1))*sin(2*pi*p2);
                         noise_gauss_complex(noise_row,noise_column)=(noise_gauss_real(noise_row,noise_column)+j*noise_gauss_imag(noise_row,noise_column))/sqrt(2);
                    end 
               end