云建站自动建站系统源码,云seo,wordpress插件html5,网站前台后台盲源分离简单的来讲就是在不明确系统的传输特性的前提下#xff0c;从系统的源信号估计出观测信号的传输信道。 假设n个未知的源信号#xff0c;各传感器接收到m个混合的观测信号#xff0c;为混入的加性噪声#xff0c;混合系统A为未知的混合矩阵。经过分离系统W后分离出近… 盲源分离简单的来讲就是在不明确系统的传输特性的前提下从系统的源信号估计出观测信号的传输信道。 假设n个未知的源信号各传感器接收到m个混合的观测信号为混入的加性噪声混合系统A为未知的混合矩阵。经过分离系统W后分离出近似与源信号的估计向盲源分离的数学模型可以表达为 要想分离出源信号S(t)的估计向量Y(t)主要是要求分解离矩阵WY(t)的分离系统过程表达式如下 盲信号分离的原理框图如图所示由于混合系统A和源信号S(t)都是未知的所以对于分离出的估计向量Y(t)可能在幅度大小和排列次序存在不确定性但是信号的信息存在于信号的波形中所以并不影响对信号的分离。 %---------------------------------------------------------------- clc clear all %% --------------------------------- Set Parameters N 1; %The number of observed mixtures Ns 2; %The number of independent sources Ls 1000; %Sample size, i.e.: number of observations finalTime 40*pi; %Final sample time (s) initialTime 0; %Initial sample time (s) %% --------------------------------- Generating Data for SSA-ICA Amix rand(N,Ns); %Amix is a random N x Ns mixing matrix timeVector initialTime:(finalTime-initialTime)/(Ls-1):finalTime; %Vector of time coordinates source1 sin(1.1*timeVector); %Independent source component 1, sin(a * t) source2 cos(0.25*timeVector); %Independent source component 2, cos(b * t) S [source1;source2]; %Source Matrix figure plot(timeVector,source1) %Plotting the N independent sources vs. time xlabel(time (s)) ylabel(Signal Amplitude) legend(source 1) figure plot(timeVector,source2) %Plotting the N independent sources vs. time xlabel(time (s)) ylabel(Signal Amplitude) legend(source 2) Yobs Amix*S; %Matrix consisting of M samples of N observed mixtures figure plot(timeVector,Yobs) %Plotting the observed signal vs. time xlabel(time (s)) ylabel(Signal Amplitude) legend(observed signal) %% --------------------------------- Call SSA-ICA algorithm M 200; Sest SSA_ICA(Yobs,Ns,M); %% --------------------------------- Show results figure plot(timeVector, Sest(1,:)) xlabel(time (s)) ylabel(Signal Amplitude) legend(Source Estimation 1) figure plot(timeVector, Sest(2,:)) xlabel(time (s)) ylabel(Signal Amplitude) legend(Source Estimation 2)
