明白了，谢谢 现在我是一点也看不懂，不过我相信以后就会懂了。 hr(1)=(wl-wh)/pi;
hr(2:M+1)=(sin(wl*k)-sin(wh*k))./(pi*k).*w;
hi(1)=0;
hi(2:M+1)=(cos(wl*k)-cos(wh*k))./(pi*k).*w;
程序是不是有误，对照丁康的论文，缺少了k=1时的值，却多了k=M+1时的值？
晕了，看明白了，不好意思
晕了，看明白了，不好意思zoomfft函数不能调用
怎么会出现“??? Undefined command/function 'zoomfft'.“，高手教我！回复 50楼 JLBhaidao 的帖子
使用which zoomfft检查下不是无此函数就是路径没设好 弱弱的问一下
for k=1:N
kk=(k-1)*D+M+(i-1)*N;
xrz(k)=x(kk+1)*hr(1)+sum(hr(2:M+1).*(x(kk+2:kk+M+1)+x(kk:-1:kk-M+1)));
xiz(k)=x(kk+1)*hi(1)+sum(hi(2:M+1).*(x(kk+2:kk+M+1)-x(kk:-1:kk-M+1)));
end
这段程序是在进行滤波和抽取吗？ 学习了:lol
回复 楼主 yangzj 的帖子
请教给位仁兄，我运行这个程序时，会出现以下错误信号，不知为何？请指教，多谢！??? function =ZoomFFT(x,fs,N,fe,D,L,M)
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Error: Function definitions are not permitted at the prompt or in scripts. 学习了:@L 还是不太懂 谢谢楼主，拷下来学习了 回复 shanghai 的帖子
牛
{:{39}:} 纠结这个细化谱好几天了，也找了网上和书上其它的matlab源程序，但改写C#始终不对。。。今天用楼主的这个就对了~~非常感谢。。 private ArrayList m_TimeSeries = new ArrayList(); //存储时域信号的数据序列
private ArrayList m_ZoomFFTSeries = new ArrayList(); //存储细化谱的数据序列
//tempTr、tempTi暂存kbfft()的入口数据；tempFr、tempFi存储kbfft()返回的实部和虚部
private ArrayList tempTr = new ArrayList(), tempTi = new ArrayList(), tempFr = new ArrayList(), tempFi = new ArrayList();
private double m_Xstep, m_ZoomXmax, m_ZoomXmin;
public ArrayList TimeSeries
{
get{ return m_TimeSeries; }
set{ m_TimeSeries = value; }
}
public ArrayList ZoomFFTSeries
{
get { return m_ZoomFFTSeries; }
set { m_ZoomFFTSeries = value; }
}
public double Xstep
{
get { return m_Xstep; }
set { m_Xstep = value; }
}
public double ZoomXmax
{
get { return m_ZoomXmax; }
set { m_ZoomXmax = value; }
}
public double ZoomXmin
{
get { return m_ZoomXmin; }
set { m_ZoomXmin = value; }
}
#region 细化谱序列
/// <summary>
/// 通过时域信号的数据序列，计算细化谱数据序列。fin为分析中心频率，iN为做谱点数，scale细化倍数，iL平均段数，iM为滤波器半阶数
/// </summary>
public void GetZoomFFTSeries(double fin, int scale, int iN, int iL, int iM)//
{
m_ZoomFFTSeries.Clear();
double[] w = new double;
for (int i = 0; i < iM; i++)
{
w = 0.5 + 0.5 * Math.Cos(Math.PI * (i+1) / iM);//Hanning窗
}
double fl, fh;//最小截止频率、最大截止频率
//fl = (fin - m_SampleHz / (4.0 * scale)) > (-m_SampleHz / 2.2) ? (fin - m_SampleHz / (4 * scale)) : -m_SampleHz / 2.2;
//fh = (fin + m_SampleHz / (4.0 * scale)) < (m_SampleHz / 2.2) ? (fin + m_SampleHz / (4 * scale)) : m_SampleHz / 2.2;
fl = fin - m_SampleHz / (4.0 * scale); fh = fin + m_SampleHz / (4.0 * scale);
double yf = scale * fl;//移频量
double df = m_SampleHz / scale / iN;//
double[] xz = new double;
double wl = 2 * Math.PI * fl / m_SampleHz, wh = 2 * Math.PI * fh / m_SampleHz;
double[] hr = new double, hi = new double;
hr = (wl - wh) / Math.PI; hi = 0;
for (int i = 1; i <= iM; i++)
{
hr = (Math.Sin(wl * i) - Math.Sin(wh * i)) / (Math.PI * i) * w;
hi = (Math.Cos(wl * i) - Math.Cos(wh * i)) / (Math.PI * i) * w;
}
double[] w1 = new double;
for (int i = 0; i < iN; i++)
{
w1 = 0.5 - 0.5 * Math.Cos(2 * Math.PI * i / iN);
}
double[] xrz = new double, xiz = new double,xzt=new double;
double[] x = new double;
for (int i = 0; i < m_TimeSeries.Count; i++)
{
x = Convert.ToDouble(m_TimeSeries);
}
double factor = -2 * Math.PI * yf / m_SampleHz;
for (int i = 1; i <= iL; i++)
{
for (int k = 1; k <= iN; k++)
{
int kk = (k - 1) * scale + iM + (i - 1) * iN;
double sumR = 0, sumI = 0;
for (int j = 1; j <= iM; j++)
{
sumR += hr * (x + x);
sumI += hi * (x - x);
}
xrz=(x * hr + sumR);
xiz=(x * hi + sumI);
}
double sumXZTr = 0, sumXZTi = 0;
for (int k = 0; k < 2*iN; k+=2)
{
xzt = xrz * Math.Cos(k * factor / 2) - xiz * Math.Sin(k * factor / 2);//偶数位为实部，奇数位为虚部
xzt = xiz * Math.Cos(k * factor / 2) + xrz * Math.Sin(k * factor / 2);
xzt = xzt * w1;
xzt = xzt * w1;
sumXZTr += xzt; sumXZTi += xzt;
}
tempTr.Clear(); tempTi.Clear();
for (int k = 0; k < 2 * iN; k += 2)
{
tempTr.Add(xzt - sumXZTr / iN);
tempTi.Add(xzt - sumXZTi / iN);
}
kbfft(tempTr.Count, 0, 0);
for (int k = 0; k < iN / 2; k++)
{
double temR = Convert.ToDouble(tempFr), temI = Convert.ToDouble(tempFi);
xz += Math.Pow(Math.Sqrt(temI * temI + temR * temR) / iN * 2, 2);
}
}
for (int i = 0; i < iN / 2; i++)
{
m_ZoomFFTSeries.Add(Math.Sqrt(xz / iL));
}
m_ZoomXmin = fl; m_ZoomXmax = fh;
m_Xstep = (fh - fl) / iN * 2;
}
/// <summary>
/// GetZoomFFTSeries(double fin, int scale, int iN, int iL, int iM)的重构方法。
/// </summary>
public void GetZoomFFTSeries()
{
this.GetZoomFFTSeries(500, 5, 1024, 1, m_TimeSeries.Count/16);
}
#endregion
#region 快速傅里叶变换方法
/***************************************************************************
* 入口参数：
*l: l = 0, 傅立叶变换; l = 1, 逆傅立叶变换
* il: il = 0,不计算傅立叶变换或逆变换模和幅角；il = 1,计算模和幅角
* n: 输入的点数，为偶数，一般为32，64，128，...,1024等
* k: 满足n=2^k(k>0),实质上k是n个采样数据可以分解为偶次幂和奇次幂的次数
* pr[]: l=0时，存放N点采样数据的实部
* l=1时, 存放傅立叶变换的N个实部
* pi[]: l=0时，存放N点采样数据的虚部
* l=1时, 存放傅立叶变换的N个虚部
*
* 出口参数：
* fr[]: l=0, 返回傅立叶变换的实部
* l=1, 返回逆傅立叶变换的实部
* fi[]: l=0, 返回傅立叶变换的虚部
* l=1, 返回逆傅立叶变换的虚部
* pr[]: il = 1,l = 0 时，返回傅立叶变换的模
* il = 1,l = 1 时，返回逆傅立叶变换的模
* pi[]: il = 1,l = 0 时，返回傅立叶变换的辐角
* il = 1,l = 1 时，返回逆傅立叶变换的辐角
***************************************************************************/
/// <summary>
/// 快速傅里叶变换方法。
/// </summary>
void kbfft(int n,int l, int il)
{
int k = (int)Math.Log(n, 2);
double[] pr = new double, pi = new double, fr = new double, fi = new double;
for (int i = 0; i < n; i++)
{
if (i < tempTr.Count)
pr = Convert.ToDouble(tempTr);
else
pr = 0;
if (i < tempTi.Count)
pi = Convert.ToDouble(tempTi);
else
pi = 0;
}
intm, is1, j, nv;
double p, q, s, vr, vi, poddr, poddi;
//排序
for (int it = 0; it <= n - 1; it++)
{
m = it; is1 = 0;
for (int i = 0; i <= k - 1; i++)
{
j = m / 2; is1 = 2 * is1 + (m - 2 * j); m = j;
fr = pr; fi = pi;
}
}
//蝶形运算
pr = 1.0; pi = 0.0;
p = Math.PI * 2 / (1.0 * n);
pr = Math.Cos(p); pi = -Math.Sin(p);
if (l != 0) pi = -pi;
for (int i = 2; i <= n - 1; i++)
{
p = pr * pr; q = pi * pi;
s = (pr + pi) * (pr + pi);
pr = p - q; pi = s - p - q;
}
for (int it = 0; it <= n - 2; it = it + 2)
{
vr = fr; vi = fi;
fr = vr + fr; fi = vi + fi;
fr = vr - fr; fi = vi - fi;
}
m = n / 2; nv = 2;
for (int l0 = k - 2; l0 >= 0; l0--)
{
m = m / 2; nv = 2 * nv;
for (int it = 0; it <= (m - 1) * nv; it = it + nv)
for ( j = 0; j <= (nv / 2) - 1; j++)
{
p = pr * fr;
q = pi * fi;
s = pr + pi;
s = s * (fr + fi);
poddr = p - q; poddi = s - p - q;
fr = fr - poddr;
fi = fi - poddi;
fr = fr + poddr;
fi = fi + poddi;
}
}
if (l != 0)
for (int i = 0; i <= n - 1; i++)
{
fr = fr / (1.0 * n);
fi = fi / (1.0 * n);
}
if (il != 0)
for (int i = 0; i <= n - 1; i++)
{
pr = Math.Sqrt(fr * fr + fi * fi);
pr = (pr / (n / 2)); //各次谐波幅值，其中pr为基波幅值
if (Math.Abs(fr) < 0.000001 * Math.Abs(fi))
{
if ((fi * fr) > 0) pi = 90.0;
else pi = -90.0;
}
else
pi = Math.Atan(fi / fr) * 360.0 / (2*Math.PI);
}
tempFi.Clear(); tempFr.Clear();
for (int i = 0; i < n; i++)
{
tempFr.Add(fr);
tempFi.Add(fi);
}
}
#endregion这是我写的一个类（其中一部分）。。。希望对大家有所帮助