%========================開始提取加噪信號(hào)的各類特征值================================
for n=1:1:50;
m=n*Ns;
x=(n-1)*Ns;
for i=x+1:m; %提取加噪信號(hào)'signal_with_noise=y+noise'的前256個(gè)元素，抽取50次
y0(i)=signal_with_noise(i);
end
Y=fft(y0); %對(duì)調(diào)制信號(hào)進(jìn)行快速傅里葉算法（離散）
y1=hilbert(y0) ; %調(diào)制信號(hào)實(shí)部的解析式
factor=0; %開始求零中心歸一化瞬時(shí)幅度譜密度的最大值gamma_max
for i=x+1:m;
factor=factor+y0(i);
end
ms=factor/(m-x);
an_i=y0./ms;
acn_i=an_i-1;
end
gamma_max=max(fft(acn_i.*acn_i))/Ns
%========================開始提取加噪信號(hào)的各類特征值================================
for n=1:1:50;
m=n*Ns;
x=(n-1)*Ns;
for i=x+1:m; %提取加噪信號(hào)'signal_with_noise=y+noise'的前256個(gè)元素，抽取50次
y0(i)=signal_with_noise(i);
end
Y=fft(y0); %對(duì)調(diào)制信號(hào)進(jìn)行快速傅里葉算法（離散）
y1=hilbert(y0) ; %調(diào)制信號(hào)實(shí)部的解析式
factor=0; %開始求零中心歸一化瞬時(shí)幅度譜密度的最大值gamma_max
for i=x+1:m;
factor=factor+y0(i);
end
ms=factor/(m-x);
an_i=y0./ms;
acn_i=an_i-1;
end
gamma_max=max(fft(acn_i.*acn_i))/Ns
