// Segundo Filtro de Kalman (um pouco melhor)
#region FiltroKalman2(double valor)
private double FiltroKalman2(double valor)
{
double retorno;
Kalman1D k = new Kalman1D();
k.Reset(
(double)0.1,
(double)0.1,
(double)0.1,
(double)400, 0);
// Assume we get to see every other measurement we calculated, and use
// the others as the points to compare for estimates.
// Run the filter, note our time unit is 1.
double[] kalman = new double[data_array_Y.Length];
double[] vel = new double[data_array_Y.Length];
double[] kGain = new double[data_array_Y.Length];
for (int i = 0; i < data_array_Y.Length; i += 2)
{
if (i == 0)
{
kalman[0] = 0;
vel[0] = k.Velocity;
kGain[0] = k.LastGain;
kalman[1] = k.Predicition(1);
vel[1] = k.Velocity;
kGain[1] = k.LastGain;
}
else
{
kalman[i] = k.Update(data_array_Y[i], 1);
kalman[i + 1] = kalman[i];
vel[i] = k.Velocity;
kGain[i] = k.LastGain;
vel[i + 1] = vel[i];
kGain[i + 1] = kGain[i];
// kalman[i + 1] = k.Predicition(1);
}
}
retorno = kalman[kalman.Length - 1];
return(retorno);
}
private double FiltroKalman2(double valor)
{
double retorno;
Kalman1D k = new Kalman1D();
k.Reset(
(double)0.1,
(double)0.1,
(double)0.1,
(double)400, 0);
// Assume we get to see every other measurement we calculated, and use
// the others as the points to compare for estimates.
// Run the filter, note our time unit is 1.
double[] kalman = new double[data_array_Y.Length];
double[] vel = new double[data_array_Y.Length];
double[] kGain = new double[data_array_Y.Length];
for (int i = 0; i < data_array_Y.Length; i += 2)
{
if (i == 0)
{
kalman[0] = 0;
vel[0] = k.Velocity;
kGain[0] = k.LastGain;
kalman[1] = k.Predicition(1);
vel[1] = k.Velocity;
kGain[1] = k.LastGain;
}
else
{
kalman[i] = k.Update(data_array_Y[i], 1);
kalman[i + 1] = kalman[i];
vel[i] = k.Velocity;
kGain[i] = k.LastGain;
vel[i + 1] = vel[i];
kGain[i + 1] = kGain[i];
// kalman[i + 1] = k.Predicition(1);
}
}
retorno = kalman[kalman.Length - 1];
return retorno;
}
