IIR filter test

1. Use matlab to generate parameters.

static float test_f(float input)
{
	static float xv[2+1], yv[2+1];
	xv[0] = xv[1]; xv[1] = xv[2]; 
	xv[2] = input/ 1.949595788e+02;
	yv[0] = yv[1]; yv[1] = yv[2]; 
	yv[2] =   (xv[0] + xv[2]) + 2 * xv[1]
				 + ( -0.8079495914 * yv[0]) + (  1.7874325180 * yv[1]);
	return yv[2];
}

static float iir_filter_matab(float input)
{
	//y[n] = 1*x[n] + 2*x[n-1] + 1*x[n-2] - (-1.1429805025)*y[n-1] - (0.412801596)*y[n-2]
	// matlab 都是二阶滤波级联
	double gain =  0.005129268366107;
	float table_N[3] = {1,2,1};
	float table_D[3] = { 1,   -1.787432517956,   0.8079495914209};
	static float xv[3], yv[3];
	xv[2] = input*gain;
	xv[0] = xv[1]; xv[1] = xv[2]; 
	yv[0] = yv[1]; yv[1] = yv[2]; 
	yv[2] = table_N[0]*xv[2]+table_N[1]*xv[1]+table_N[2]*xv[0] - table_D[1]*yv[1] - table_D[2]*yv[0];
	
	return yv[2]; 
}

   y [n] = b0⋅x [n] + b1⋅x [n − 1] + b2⋅x [n − 2] −a1⋅y [n − 1] −a2⋅y [n − 2]

High-order filtering in matlab is cascaded with second-order filtering.

The filtering effect of the code generated by the test and the web version is basically the same.

 

Web version generates C language

https://www-users.cs.york.ac.uk/~fisher/mkfilter/trad.html

Summarize the variance as the data dispersion.