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C#实现FFT(递归法)

原作者: [db:作者] 来自: [db:来源] 收藏 邀请

C#实现FFT(递归法)

1. C#实现复数类

我们在进行信号分析的时候,难免会使用到复数。但是遗憾的是,C#没有自带的复数类,以下提供了一种复数类的构建方法。

复数相比于实数,可以理解为一个二维数,构建复数类,我们需要实现以下这些内容:

  1. 复数实部与虚部的属性
  2. 复数与复数的加减乘除运算
  3. 复数与实数的加减乘除运算
  4. 复数取模
  5. 复数取相位角
  6. 欧拉公式(即\(e^{ix+y}\)

C#实现的代码如下:

 public class Complex
    {
        double real;
        double imag;
        public Complex(double x, double y)   //构造函数
        {
            this.real = x;
            this.imag = y;
        }
        //通过属性实现对复数实部与虚部的单独查看和设置
        public double Real
        {
            set { this.real = value; }
            get { return this.real; }
        }
        public double Imag
        {
            set { this.imag = value; }
            get { return this.imag; }
        }
        //重载加法
        public static Complex operator +(Complex c1, Complex c2)
        {
            return new Complex(c1.real + c2.real, c1.imag + c2.imag);
        }
        public static Complex operator +(double c1, Complex c2)
        {
            return new Complex(c1 + c2.real, c2.imag);
        }
        public static Complex operator +(Complex c1, double c2)
        {
            return new Complex(c1.Real + c2, c1.imag);
        }
        //重载减法
        public static Complex operator -(Complex c1, Complex c2)
        {
            return new Complex(c1.real - c2.real, c1.imag - c2.imag);
        }
        public static Complex operator -(double c1, Complex c2)
        {
            return new Complex(c1 - c2.real, -c2.imag);
        }
        public static Complex operator -(Complex c1, double c2)
        {
            return new Complex(c1.real - c2, c1.imag);
        }
        //重载乘法
        public static Complex operator *(Complex c1, Complex c2)
        {
            double cr = c1.real * c2.real - c1.imag * c2.imag;
            double ci = c1.imag * c2.real + c2.imag * c1.real;
            return new Complex(Math.Round(cr, 4), Math.Round(ci, 4));
        }
        public static Complex operator *(double c1, Complex c2)
        {
            double cr = c1 * c2.real;
            double ci = c1 * c2.imag;
            return new Complex(Math.Round(cr, 4), Math.Round(ci, 4));
        }
        public static Complex operator *(Complex c1, double c2)
        {
            double cr = c1.Real * c2;
            double ci = c1.Imag * c2;
            return new Complex(Math.Round(cr, 4), Math.Round(ci, 4));
        }

        //重载除法
        public static Complex operator /(Complex c1, Complex c2)
        {
            if (c2.real == 0 && c2.imag == 0)
            {
                return new Complex(double.NaN, double.NaN);
            }
            else
            {
                double cr = (c1.imag * c2.imag + c2.real * c1.real) / (c2.imag * c2.imag + c2.real * c2.real);
                double ci = (c1.imag * c2.real - c2.imag * c1.real) / (c2.imag * c2.imag + c2.real * c2.real);
                return new Complex(Math.Round(cr, 4), Math.Round(ci, 4));           //保留四位小数后输出
            }
        }
      
        public static Complex operator /(double c1, Complex c2)
        {
            if (c2.real == 0 && c2.imag == 0)
            {
                return new Complex(double.NaN, double.NaN);
            }
            else
            {
                double cr = c1 * c2.Real / (c2.imag * c2.imag + c2.real * c2.real);
                double ci = -c1 * c2.imag / (c2.imag * c2.imag + c2.real * c2.real);
                return new Complex(Math.Round(cr, 4), Math.Round(ci, 4));           //保留四位小数后输出
            }
        }
      
        public static Complex operator /(Complex c1, double c2)
        {
            if (c2 == 0)
            {
                return new Complex(double.NaN, double.NaN);
            }
            else
            {
                double cr = c1.Real / c2;
                double ci = c1.imag / c2;
                return new Complex(Math.Round(cr, 4), Math.Round(ci, 4));           //保留四位小数后输出
            }
        }
        //创建一个取模的方法
        public static double Abs(Complex c)
        {
            return Math.Sqrt(c.imag * c.imag + c.real * c.real);
        }
        //创建一个取相位角的方法
        public static double Angle(Complex c)
        {
            return Math.Round(Math.Atan2(c.real, c.imag), 6);//保留6位小数输出
        }
        //重载字符串转换方法,便于显示复数
        public override string ToString()
        {
            if (imag >= 0)
                return string.Format("{0}+i{1}", real, imag);
            else
                return string.Format("{0}-i{1}", real, -imag);
        }
        //欧拉公式
        public static Complex Exp(Complex c)
        {
            double amplitude = Math.Exp(c.real);
            double cr = amplitude * Math.Cos(c.imag);
            double ci = amplitude * Math.Sin(c.imag);
            return new Complex(Math.Round(cr, 4), Math.Round(ci, 4));//保留四位小数输出
        }
    }

2. 递归法实现FFT

以下的递归法是基于奇偶分解实现的。

奇偶分解的原理推导如下:

\[\begin{split} X(k)=DFT[x(n)]&=\sum_{n=0}^{N-1}x(n)W_N^{nk}\\ &=\sum_{r=0}^{N/2-1}x(2r)W_N^{2rk}+\sum_{r=0}^{N/2-1}x(2r+1)W_N^{(2r+1)k},将x(n)按奇偶分解\\ &=\sum_{r=0}^{N/2-1}x(2r)(W_N^{2})^{rk}+W_N^k\sum_{r=0}^{N/2-1}x(2r+1)(W_N^2)^{rk} \end{split} \]

\(x(2r)\)\(x(2r+1)\)都是长度为\(N/2-1\)的数据序列,不妨令

\[x_1(n)=x(2r)\\ x_2(n)=x(2r+1) \]

则原来的DFT就变成了:

\[\begin{split} X(k)&=\sum_{n=0}^{N/2-1}x_1(n)(W_N^{2})^{nk}+W_N^k\sum_{n=0}^{N/2-1}x_2(n)(W_N^2)^{nk}\\ &=F(x_1(n))+W_N^kF(x_2(n))\\ &=X_1(k)+W_N^kX_2(k) \end{split} \]

于是,将原来的N点傅里叶变换变成了两个N/2点傅里叶变换的线性组合。

但是,N/2点傅里叶变换只能确定N/2个频域数据,另外N/2个数据怎么确定呢?

因为\(X_1(k)\)\(X_2(k)\)周期都是\(N/2\),所以有

\[X_1(k+N/2)=X_1(k),X_2(k+N/2)=X_2(k)\\ W_N^{k+N/2}=-W_N^k\\ \]

从而得到:

\[\begin{split} X(k+N/2)&=X_1(k+N/2)+W_N^{k+N/2}X_2(k+N/2)\\ &=X_1(k)-W_n^kX_2(k) \end{split} \]

综上,我们就可以得到递归法实现FFT的流程:

  1. 对于每组数据,按奇偶分解成两组数据

  2. 两组数据分别进行傅里叶变换,得到\(X_1(k)\)\(X_2(k)\)

  3. 总体数据的\(X(k)\)由下式确定:

    \[X(k)==X_1(k)+W_N^kX_2(k)\\ X(k+N/2)=X_1(k)-W_n^kX_2(k)\\ 0\le k \le N/2 -1 \]

  4. 对上述过程进行递归

具体代码实现如下:

public Complex[] FFTre(Complex[] c)
{
    int n = c.Length;
    Complex[] cout = new Complex[n];
    if (n == 1)
    {
        cout[0] = c[0];
        return cout;
    }
    else
    {
        double n_2_f = n / 2;
        int n_2 = (int)Math.Floor(n_2_f);
        Complex[] c1 = new Complex[n / 2];
        Complex[] c2 = new Complex[n / 2];
        for (int i = 0; i < n_2; i++)
        {
            c1[i] = c[2 * i];
            c2[i] = c[2 * i + 1];
        }
        Complex[] c1out = FFTre(c1);
        Complex[] c2out = FFTre(c2);
        Complex[] c3 = new Complex[n / 2];
        for (int i = 0; i < n / 2; i++)
        {
            c3[i] = new Complex(0, -2 * Math.PI * i / n);
        }
        for (int i = 0; i < n / 2; i++)
        {
            c2out[i] = c2out[i] * Complex.Exp(c3[i]);
        }

        for (int i = 0; i < n / 2; i++)
        {
            cout[i] = c1out[i] + c2out[i];
            cout[i + n / 2] = c1out[i] - c2out[i];
        }
        return cout;
    }
}

3. 补充:窗函数

顺便提供几个常用的窗函数:

  • Rectangle
  • Bartlett
  • Hamming
  • Hanning
  • Blackman
    public class WDSLib
    {
        //以下窗函数均为periodic
        public double[] Rectangle(int len)
        {
            double[] win = new double[len];
            for (int i = 0; i < len; i++)
            {
                win[i] = 1;
            }
            return win;
        }

        public double[] Bartlett(int len)
        {
            double length = (double)len - 1;
            double[] win = new double[len];
            for (int i = 0; i < len; i++)
            {
                if (i < len / 2) { win[i] = 2 * i / length; }
                else { win[i] = 2 - 2 * i / length; }
            }
            return win;
        }

        public double[] Hamming(int len)
        {
            double[] win = new double[len];
            for (int i = 0; i < len; i++)
            {
                win[i] = 0.54 - 0.46 * Math.Cos(Math.PI * 2 * i / len);
            }
            return win;
        }

        public double[] Hanning(int len)
        {
            double[] win = new double[len];
            for (int i = 0; i < len; i++)
            {
                win[i] = 0.5 * (1 - Math.Cos(2 * Math.PI * i / len));
            }
            return win;
        }

        public double[] Blackman(int len)
        {
            double[] win = new double[len];
            for (int i = 0; i < len; i++)
            {
                win[i] = 0.42 - 0.5 * Math.Cos(Math.PI * 2 * (double)i / len) + 0.08 * Math.Cos(Math.PI * 4 * (double)i / len);
            }
            return win;
        }
    }

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