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人工智能网站建设,中国数据网站空间,深圳深度网站建设,南山网站设计费用#x1f4a5;#x1f4a5;#x1f49e;#x1f49e;欢迎来到本博客❤️❤️#x1f4a5;#x1f4a5; #x1f3c6;博主优势#xff1a;#x1f31e;#x1f31e;#x1f31e;博客内容尽量做到思维缜密#xff0c;逻辑清晰#xff0c;为了方便读者。 ⛳️座右铭欢迎来到本博客❤️❤️ 博主优势博客内容尽量做到思维缜密逻辑清晰为了方便读者。 ⛳️座右铭行百里者半于九十。 本文目录如下 目录 1 概述 2 运行结果 3 Matlab代码实现 4 参考文献 1 概述 本文可塑造性强。 本文介绍了高分辨率多传感器时频分布MTFD及其在多通道非平稳信号分析中的应用。该方法结合了高分辨率时频分析和阵列信号处理方法。MTFD的性能通过多种应用进行了演示包括基于到达方向DOA估计的源定位和非平稳源的自动组分分离ACS重点是盲源分离。MTFD方法通过脑电图信号的两个应用进一步说明。一种专门使用 ACS 和 DOA 估计方法进行伪影去除和源定位。另一种使用MTFD进行跨渠道因果关系分析。本文可以换上自己的数据并将这些方法实现。 2 运行结果 部分代码 clear; close all; clc; addpath(genpath(Supporting functions)); %% Main %% Extracting SNR information from Data Folders S dir(../data/DOA Data); SNR_N sum([S(ismember({S.name},{.,..})).isdir]); SNR_i zeros(1,SNR_N); for i 1:SNR_N     [, r1] strtok(S(7i).name);     [s1, rrr] strtok(r1);     SNR_i(1,i) str2double(s1); end SNR_i sort(SNR_i); %% MUSIC and TF-MUSIC Spectrum SNR_N 2; figure(Color,[1 1 1],Position,[100, 70, 900 500]); ha tight_subplot(SNR_N/2,2,[0.05 0.01],[0.12 0.12],[0.08 0.08]); for i 1:SNR_N     Path [../data/DOA Data/SNR num2str(SNR_i(i)), dB];     load([Path /Averaged_Spectrum]);     axes(ha(i));     plot(theta, P_tf_music_avg,-b,linewidth,2); hold on;     plot(theta, P_music_avg,-.r,linewidth,2); hold on;     plot(repmat(ra(1),10),linspace(0,1.2,10),k–,linewidth,2); hold on     plot(repmat(ra(2),10),linspace(0,1.2,10),k–,linewidth,2); grid on     axis([0 39.9 0 1.2])     if(mod(i,2) i SNR_N-1)         set(gca,XTickLabel,,fontweight,bold,fontsize,13);         ylabel(P_M_U_S_I_C (\theta),Fontsize,16);         title([SNR num2str(SNR_i(i)), dB],fontsize,20);     elseif i SNR_N-1         set(gca,fontweight,bold,fontsize,13);         ylabel(P_M_U_S_I_C (\theta),Fontsize,16);         xlabel(\theta (deg),Fontsize,16);         title([SNR num2str(SNR_i(i)), dB],fontsize,20);     elseif i SNR_N         set(gca,YTickLabel,,fontweight,bold,fontsize,13);         xlabel(\theta (deg),Fontsize,16);         title([SNR num2str(SNR_i(i)), dB],fontsize,20);         legend(TF-MUSIC Averaged Spectrum,MUSIC Averaged Spectrum,True Angles,Location,Southwest)     else         set(gca,XTickLabel,,YTickLabel,,fontweight,bold);         title([SNR num2str(SNR_i(i)), dB],fontsize,20);     end end set(gcf,Units,inches); screenposition get(gcf,Position); set(gcf,PaperPosition,[0 0 screenposition(3:4)],PaperSize,screenposition(3:4)); %% DOA NMSE SNR_N sum([S(~ismember({S.name},{.,..})).isdir]); Path [../data/DOA Data/SNR num2str(SNR_i(i)), dB]; load([Path /DOA],ra); nmse_music     zeros(length(ra), SNR_N); nmse_tf_music   zeros(length(ra), SNR_N); nmse_esprit     zeros(length(ra), SNR_N); nmse_tf_esprit zeros(length(ra), SNR_N); music_rate     zeros(length(ra), SNR_N); tf_music_rate zeros(length(ra), SNR_N); for i 1:SNR_N     Path [../data/DOA Data/SNR num2str(SNR_i(i)), dB];     load([Path /DOA]);     for j 1:length(ra)         temp1 DOA_music(:,j); temp2 DOA_tf_music(:,j);         music_rate(j,i)     sum(temp1~0)/length(temp1);         tf_music_rate(j,i) sum(temp2~0)/length(temp2);         temp1 temp1(temp1~0); temp2 temp2(temp2~0);         nmse_music(j,i)     (mean(((temp1 - ra(j))/ra(j)).^2));         nmse_tf_music(j,i)   (mean(((temp2 - ra(j))/ra(j)).^2));         nmse_esprit(j,i)     (mean(((DOA_esprit(:,j) - ra(j))/ra(j)).^2));         nmse_tf_esprit(j,i) (mean(((DOA_tf_esprit(:,j) - ra(j))/ra(j)).^2));     end end fprintf(2,Mean Probability of Detection (Pd)\n); fprintf(MUSIC     : %0.3f\n,mean(mean(music_rate))); fprintf(TF_MUSIC  : %0.3f\n,mean(mean(tf_music_rate))) fprintf(ESPRIT    : %0.1f\n,1) fprintf(TF_ESPRIT : %0.1f\n,1) figure(Color,[1 1 1],Position,[100, 10, 650, 550]); ha tight_subplot(1,1,[0.01 0.01],[0.12 0.1],[0.12 0.12]); axes(ha(1)); plot(SNR_i,10*log10(mean(nmse_tf_music)),-b,linewidth,2);  hold on; plot(SNR_i,10*log10(mean(nmse_music)),–b,linewidth,2);    hold on; plot(SNR_i,10*log10(mean(nmse_tf_esprit)),-.r,linewidth,2); hold on; plot(SNR_i,10*log10(mean(nmse_esprit)),:r,linewidth,2);   grid on; xlim([SNR_i(1) SNR_i(end)]); legend(TF-MUSIC,MUSIC,TF-ESPRIT,ESPRIT,Location,Southwest); set(gca,fontweight,bold,fontsize,14); title(DOA Normalized Mean Square Error,fontsize,18); xlabel(SNR (dB)); ylabel(NMSE (dB)); set(gcf,Units,inches); screenposition get(gcf,Position); set(gcf,PaperPosition,[0 0 screenposition(3:4)],PaperSize,screenposition(3:4)); %% DOA PDFs SNR_N 2; bins_N 100; for i 1:SNR_N     aa figure(Color,[1 1 1],Position,[100, 0, 650, 700]);     ha tight_subplot(2,1,[0.01 0.01],[0.1 0.1],[0.05 0.05]);     Path [../data/DOA Data/SNR num2str(SNR_i(i)), dB];     load([Path /DOA]);     axes(ha(1)); temp DOA_music; temp temp(temp~0);     histogram(temp, bins_N,Normalization,probability,facecolor,[1 0.84 0],linewidth,1.2); hold on;     temp DOA_tf_music; temp temp(temp~0);     histogram(temp, bins_N,Normalization,probability,…         facecolor,[1 0 0],linewidth,1.2); xlim([0 40]);     temp1 DOA_music(:,1); temp1 temp1(temp1~0); u1 mean(temp1); s std(temp1);     tag11 [\mu_1 , num2str(round(u1,1)), , \sigma_1 , num2str(round(s,1))];     temp1 DOA_music(:,2); temp1 temp1(temp1~0); u1 mean(temp1); s std(temp1);     tag12 [\mu_2 , num2str(round(u1,1)), , \sigma_2 , num2str(round(s,1))];     tag1 [         MUSIC char(10) tag11 char(10) tag12];     temp1 DOA_tf_music(:,1); temp1 temp1(temp1~0); u1 mean(temp1); s std(temp1);     tag11 [\mu_1 , num2str(round(u1,1)), , \sigma_1 , num2str(round(s,1))];     temp1 DOA_tf_music(:,2); temp1 temp1(temp1~0); u1 mean(temp1); s std(temp1);     tag12 [\mu_2 , num2str(round(u1,1)), , \sigma_2 , num2str(round(s,1))];     tag2 [       TF-MUSIC char(10) tag11 char(10) tag12];     legend(tag1, tag2,Location,Northwest); grid on;     set(gca,XTickLabel,,YTickLabel,,fontweight,bold,fontsize,13);     title([SNR num2str(SNR_i(i)), dB],fontsize,20);     axes(ha(2));     histogram(DOA_esprit, bins_N,Normalization,probability,…         facecolor,[0 0.5 0],linewidth,1.2); hold on;     histogram(DOA_tf_esprit, bins_N,Normalization,probability,…         facecolor,[0 0.45 0.74],linewidth,1.2); xlim([0 40]);     temp1 DOA_esprit(:,1); u1 mean(temp1); s std(temp1);     tag11 [\mu_1 , num2str(round(u1,1)), , \sigma_1 , num2str(round(s,1))];     temp1 DOA_esprit(:,2); u1 mean(temp1); s std(temp1);     tag12 [\mu_2 , num2str(round(u1,1)), , \sigma_2 , num2str(round(s,1))];     tag1 [         ESPRIT char(10) tag11 char(10) tag12];     temp1 DOA_tf_esprit(:,1); u1 mean(temp1); s std(temp1);     tag11 [\mu_1 , num2str(round(u1,1)), , \sigma_1 , num2str(round(s,1))];     temp1 DOA_tf_esprit(:,2); u1 mean(temp1); s std(temp1);     tag12 [\mu_2 , num2str(round(u1,1)), , \sigma_2 , num2str(round(s,1))];     tag2 [       TF-ESPRIT char(10) tag11 char(10) tag12];     legend(tag1, tag2,Location,Northwest); grid on;     set(gca,YTickLabel,,fontweight,bold,fontsize,13);     xlabel(\theta (deg));     set(gcf,Units,inches); screenposition get(gcf,Position);     set(gcf,PaperPosition,[0 0 screenposition(3:4)],PaperSize,screenposition(3:4)); end 3 Matlab代码实现 4 参考文献 部分理论来源于网络如有侵权请联系删除。 [1] B. Boashash, A. Aissa-El-Bey, M. F. Al-Sad, Multisensor Time-Frequency Signal Processing:     A tutorial review with illustrations in selected application areas, Digital Signal Processing, In Press. [2] B. Boashash, A. Aissa-El-Bey, M. F. Al-Sad, Multisensor time-frequency signal processing software Matlab package: An analysis tool for multichannel non-stationary data , SoftwareX, In Press.