用matlab的filterDesigner设计一个四阶高通滤波器
% %
% % IIR butterworth滤波器系数% 高通（直接II型4阶、Fs = 20KHz、Fc = 1500Hz）PLC=0.0025PLC
% % 分子、分母系数（对累加和进行滤波）
b1 = [0.5365885311137, -2.146354124455, 3.219531186682, -2.146354124455, 0.5365885311137];
a1 = [1, -2.773682317549, 3.019038699424, -1.504765051425, 0.2879304294211];
% % 阶跃响应
% figure
% sys = tf(b1,a1);
% step(sys)cur_flt1 = filter(b1,a1,cur_diffsum); % 滤波处理
vol_flt1 = filter(b1,a1,vol_diffsum); % 滤波处理
figure("Name", "IIR cur_4jie1500")
plot(cur_flt1,'*');
figure("Name", "IIR vol_4jie1500")
plot(vol_flt1,'*');filter的实际就是对X数组做离散运算得到滤波后数组；
使用的离散序列为：
a(1)*y(n) = b(1)*x(n) + b(2)*x(n-1) + b(3)*x(n-2) + ... + b(nb+1)*x(n-nb)
- a(2)*y(n-1) - a(3)*y(n-2) - ... - a(na+1)*y(n-na)
%%%===================================%%%
x = cur_diffsum;
y(1)=b1(1)*x(1);
y(2)=b1(1)*x(2)+b1(2)*x(1)-a1(2)*y(1);
y(3)=b1(1)*x(3)+b1(2)*x(2)+b1(3)*x(1)-a1(2)*y(2)-a1(3)*y(1);
y(4)=b1(1)*x(4)+b1(2)*x(3)+b1(3)*x(2)+b1(4)*x(1)-a1(2)*y(3)-a1(3)*y(2)-a1(4)*y(1);
for i=5:leny(i) = b1(1)*x(i)+b1(2)*x(i-1)+b1(3)*x(i-2)+b1(4)*x(i-3)+b1(5)*x(i-4) - a1(2)*y(i-1)-a1(3)*y(i-2)-a1(4)*y(i-3)-a1(5)*y(i-4);
end
figname = figure("Name", "cur_fft_y(i)");
signal_fft(y, Fs, 256, 1, figname);
%%%===================================%%%%%%+++++++++++++++++++++++++++++++++++%%%
SumY = 0;
SumX = 0;
FltX = [0,0,0,0,0];
FltY = [0,0,0,0,0];
for i=1:lenFltX(5) = FltX(4);FltX(4) = FltX(3);FltX(3) = FltX(2);FltX(2) = FltX(1);FltX(1) = x(i);for p=1:5SumX = SumX + b1(p)*FltX(p);endfor q=2:5SumY = SumY + a1(q)*FltY(q-1);endFltY(4) = FltY(3);FltY(3) = FltY(2);FltY(2) = FltY(1);FltY(1) = (SumX - SumY);Yn(i) = FltY(1);SumY = 0;SumX = 0;
end
figname = figure("Name", "cur_fft_Yn");
signal_fft(Yn, Fs, 256, 1, figname);其C语言实现形式如下：
//% % IIR butterworth滤波器系数% 高通（直接II型4阶、Fs = 20KHz、Fc = 1500Hz）
//% % 分子、分母系数（对累加和进行滤波）
double b1[5] = { 0.5365885311137, -2.146354124455, 3.219531186682, -2.146354124455, 0.5365885311137 };
double a1[5] = { 1, -2.773682317549, 3.019038699424, -1.504765051425, 0.2879304294211 };/**
* 功能:高通滤波
*
* 参数:
* Xn: 传入的 diffsum 值
* 返回值:
* Yn: 滤波后的值
*/
double iir_filter_diffsum(double Xn)
{// 初始化滤波参数static double FltX[5] = { 0, 0, 0, 0, 0 };static double FltY[5] = { 0, 0, 0, 0, 0 };double SumX = 0, SumY = 0, Yn = 0;int i = 0;FltX[4] = FltX[3];FltX[3] = FltX[2];FltX[2] = FltX[1];FltX[1] = FltX[0];FltX[0] = Xn;for (i = 0; i < 5; i++){SumX = SumX + b1[i] * FltX[i];}for (i = 1; i < 5; i++){SumY = SumY + a1[i] * FltY[i - 1];}FltY[3] = FltY[2];FltY[2] = FltY[1];FltY[1] = FltY[0];FltY[0] = (SumX - SumY);Yn = FltY[0];return Yn;
}
滑动均值滤波：
/**
* 功能:滑动求平均
*
* 输入参数:
* data: 传入的数据
* d_size: data数据长度
* w_size: 滑动窗口大小
* 输出参数:
* result: 滤波后的值
*/
void moving_average(double *data, double* result, int d_size, int w_size)
{for (int i = 0; i < d_size - w_size + 1; i++){double sum = 0;for (int j = 0; j < w_size;j++){sum += data[i + j];}//result[i + (w_size - 1) / 2] = sum / w_size;result[i] = sum / w_size;}//将末尾的数据直接添加到平均值数组中for (int i = d_size - w_size + 1; i < d_size;i++){result[i] = data[i];}
}