function [unmixingMatrix,separationSignal] = FastICA(mixedSignal)
% FastICA算法
%
% Input :
% * mixedSignal: 混合信号构成的矩阵,即观测信号
%
% Output :
% * unmixingMatrix: is an n x m estimate of the mixing matrix
% * separationSignal: estimate of the source signals
MixedS = mixedSignal;
MixedS_bak=MixedS;
%%%%%%%%%%%%%%%%%%%%%%%%%% 标准化 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% MixedS_mean=zeros(2,1);
% for i=1:2
% MixedS_mean(i)=mean(MixedS(i,:));
% end % 计算MixedS的均值
%
% for i=1:2
% for j=1:size(MixedS,1)
% MixedS(i,j)=MixedS(i,j)-MixedS_mean(i);
% end
% end
%%%%%%%%%%%%%%%%%%%%%%%%%%% 白化 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
MixedS_cov=cov(MixedS'); % cov为求协方差的函数
[E,D]=eig(MixedS_cov); % 对信号矩阵的协方差函数进行特征值分解
Q=inv(sqrt(D))*(E)'; % Q为白化矩阵
MixedS_white=Q*MixedS; % MixedS_white为白化后的信号矩阵
IsI=cov(MixedS_white'); % IsI应为单位阵
%%%%%%%%%%%%%%%%%%%%%%%% FASTICA算法 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
X=MixedS_white; % 以下算法将对X进行操作
[VariableNum,SampleNum]=size(X);
numofIC=VariableNum; % 在此应用中,独立元个数等于变量个数
B=zeros(numofIC,VariableNum); % 初始化列向量w的寄存矩阵,B=[b1 b2 ... bd]
for r=1:numofIC
i=1;maxIterationsNum=1000; % 设置最大迭代次数(即对于每个独立分量而言迭代均不超过此次数)
IterationsNum=0;
b=rand(numofIC,1)-.5; % 随机设置b初值
b=b/norm(b); % 对b标准化 norm(b):向量元素平方和开根号
while i<=maxIterationsNum+1
if i == maxIterationsNum % 循环结束处理
fprintf('\n第%d分量在%d次迭代内并不收敛。', r,maxIterationsNum);
break;
end
bOld=b;
a2=1;
u=1;
t=X'*b;
g=t.*exp(-a2*t.^2/2);
dg=(1-a2*t.^2).*exp(-a2*t.^2/2);
b=((1-u)*t'*g*b+u*X*g)/SampleNum-mean(dg)*b;
% 核心公式
b=b-B*B'*b; % 对b正交化
b=b/norm(b);
if abs(abs(b'*bOld)-1)<1e-9 % 如果收敛,则
B(:,r)=b; % 保存所得向量b
break;
end
i=i+1;
end
end
for k=1:numofIC
W(:,k)=B(:,k)/5^k; % 得到解混矩阵W
end
%%%%%%%%%%%%%%%%%%%%%%%%%% 进行解混合 %%%%%%%%%%%%%%%%%%%%%%%%%
ICAedS=W'*Q*MixedS_bak; % 计算ICA后的矩阵
unmixingMatrix = W; %解混矩阵
separationSignal = ICAedS; %分离后的信号
end