evilestspirit 发表于 2017-3-15 20:30

指数衰减函数的fft

我写了一个程序,并将fft后的结果调整了一下,使他表示的是原始信号的幅值。
这个程序有问题吗?

<p>clear
N=2048;
fs=2000;
T=1/fs;
n=0:N-1;
f=fs/N*(0:N/2);
x=1000000*exp(-200*t);
X=fft(x);
XX(1)=abs(X(1))/N;   XX(2:N/2+1)=2*abs(X(2:N/2+1))/N;
plot(f,XX);
</p>

evilestspirit 发表于 2017-3-15 20:32

中间少了一个语句,查到5和6行中间
t=n*T;

evilestspirit 发表于 2017-3-15 20:37

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
这是图像,0频率点的幅值怎么比后面的小了。

Triste 发表于 2017-3-16 08:12


没什么问题吧

evilestspirit 发表于 2017-3-16 19:23

Triste 发表于 2017-3-16 08:12
没什么问题吧

最高点不应该在0频率地方吗?我的这个不是啊

hcharlie 发表于 2017-3-19 22:02

本帖最后由 hcharlie 于 2017-3-19 22:10 编辑

你错了!
我说的不是哪一条指令错了,而是概念错了。
你心里想的是做指数衰减函数的傅立叶变换,而实际做的是其有限长一段数据的FFT,由此后面的一切都错了。
指数衰减函数是无穷长的衰减到零的函数,但是你取其一段有限长的数据做FFT(或DFT),实际含义是做了以此段数据无限重复的周期函数的傅立叶分析,因此得到的零频,基频,2,3,4。。。阶频率,也是这个周期函数的概念,你的指数衰减函数不是一个振动信号,不存在所谓的零频,2,3,4阶的频率只有振动问题才有的概念,由于基本出发点有问题,所以结论都错了。
结论:你的指数衰减函数不能做FFT。

Agoni 发表于 2017-3-20 08:48

close all; %先关闭所有图片
Adc=2; %直流分量幅度
A1=3; %频率F1信号的幅度
A2=1.5; %频率F2信号的幅度
F1=50; %信号1频率(Hz)
F2=75; %信号2频率(Hz)
Fs=256; %采样频率(Hz)
P1=-30; %信号1相位(度)
P2=90; %信号相位(度)
N=256; %采样点数
t=; %采样时刻
%信号
S=Adc+A1*cos(2*pi*F1*t+pi*P1/180)+A2*cos(2*pi*F2*t+pi*P2/180);
%显示原始信号
plot(S);
title('原始信号');
figure;
Y = fft(S,N); %做FFT变换
Ayy = (abs(Y)); %取模
plot(Ayy(1:N)); %显示原始的FFT模值结果
title('FFT 模值');
figure;
Ayy=Ayy/(N/2); %换算成实际的幅度
Ayy(1)=Ayy(1)/2;
F=(-1)*Fs/N; %换算成实际的频率值
plot(F(1:N/2),Ayy(1:N/2)); %显示换算后的FFT模值结果
title('幅度-频率曲线图');
figure;
Pyy=;
for i="1:N/2"
Pyy(i)=phase(Y(i)); %计算相位
Pyy(i)=Pyy(i)*180/pi; %换算为角度
end;
plot(F(1:N/2),Pyy(1:N/2)); %显示相位图
title('相位-频率曲线图');

evilestspirit 发表于 2017-3-22 16:24

hcharlie 发表于 2017-3-19 22:02
你错了!
我说的不是哪一条指令错了,而是概念错了。
你心里想的是做指数衰减函数的傅立叶变换,而实际做 ...

我取的那段已经衰减到零了也不能做吗?

hcharlie 发表于 2017-3-22 17:16

本帖最后由 hcharlie 于 2017-3-22 18:54 编辑

evilestspirit 发表于 2017-3-22 16:24
我取的那段已经衰减到零了也不能做吗?
你采集频率2000,采集2048点,大概1秒,你做FFT,结果是以你的2048点无限重复的周期函数的结果,周期函数的周期大概1 秒,最低频率大概是1 Hz,看起来象1 Hz的振动,而实际你的函数是单调衰减的,根本不存在接近1 Hz的振动,也不存在接着出来的2,3,4。。。Hz 的振动,这些振幅都是周期函数的频谱,不是你的单调函数的振幅,你的单调函数没有什么振幅!
就事论事。你说这种情况下零频应该是大还是小呢?我做了几个算例,随着衰减系数的大小的不同,零频相对一阶数量是可大可小的。



evilestspirit 发表于 2017-3-23 17:40

谢谢你的回答!我的采集频率和采集点数应该取多少?
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