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137 lines (130 loc) · 5.55 KB
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using System;
using System.IO.Ports;
using MathNet.Filtering.Kalman;
using Matrix = MathNet.Numerics.LinearAlgebra.Matrix<double>;
namespace Kalman_Filter_Nuget
{
class Program
{
static void Main(string[] args)
{
SerialPort port = new SerialPort("COM3", 19200);
String CurrentReading = "ABCD";
Matrix initial = Matrix.Build.DenseOfArray(new double[,] {
{0},
{0},
{0},
{0},
{0},
{0},
{0},
{0},
{0} });
Matrix covariance = Matrix.Build.DenseOfArray(new double[,] {
{0.1,0,0,0,0,0,0,0,0},
{0,0.1,0,0,0,0,0,0,0},
{0,0,0.1,0,0,0,0,0,0},
{0,0,0,0.1,0,0,0,0,0},
{0,0,0,0,0.1,0,0,0,0},
{0,0,0,0,0,0.1,0,0,0},
{0,0,0,0,0,0,0.1,0,0},
{0,0,0,0,0,0,0,0.1,0},
{0,0,0,0,0,0,0,0,0.1 } });
DiscreteKalmanFilter kalman = new DiscreteKalmanFilter(initial, covariance);
double[] IndividualValues = { 0, 0, 0, 0, 0, 0 };
String[] SubStrings;
double x = 0, y = 0, z = 0, vx = 0, t2 = 0, x1, y1, z1, time, velx, vely, velz, accx, accy, accz;
bool correctRead;
int comaCount = 0;
char[] test;
port.ReadTimeout = 2000;
port.Close();
if (!port.IsOpen)
port.Open();
while (t2 <= 20000000000)
{
CurrentReading = port.ReadLine();
test = CurrentReading.ToCharArray();
correctRead = true;
comaCount = 0;
for (int i = test.Length - 1; i >= 0; i--)
{
if (Char.IsNumber(test[i]) == false & test[i] != ',' & test[i] != '.' & Char.IsWhiteSpace(test[i]) == false & test[i] != '-')
{
correctRead = false;
}
if (test[i] == ',')
{
comaCount = comaCount + 1;
}
}
if (correctRead == true & comaCount == 4)
{
SubStrings = CurrentReading.Split(',');
x1 = Convert.ToSingle(SubStrings[0]);
y1 = Convert.ToSingle(SubStrings[1]);
z1 = Convert.ToSingle(SubStrings[2]);
time = Convert.ToSingle(SubStrings[3]);
velx = (x1 - x) / (time - t2);
vely = (y1 - y) / (time - t2);
velz = (z1 - z) / (time - t2);
accx = velx / (time - t2);
accy = vely / (time - t2);
accz = velz / (time - t2);
//Dummy Data : Generate a very noisy position estimate
Matrix res = Matrix.Build.DenseOfArray(new double[,] {
{x1,0,0,0,0,0,0,0,0},
{0,y1,0,0,0,0,0,0,0},
{0,0,z1,0,0,0,0,0,0},
{0,0,0,velx,0,0,0,0,0},
{0,0,0,0,vely,0,0,0,0},
{0,0,0,0,0,velz,0,0,0},
{0,0,0,0,0,0,accx,0,0},
{0,0,0,0,0,0,0,accy,0},
{0,0,0,0,0,0,0,0,accz } });
Matrix Meassurement = Matrix.Build.DenseOfArray(new double[,] {
{0.1},
{0.1},
{0.1},
{0.1},
{0.1},
{0.1},
{0.1},
{0.1},
{0.1} });
Matrix Noise = Matrix.Build.DenseOfArray(new double[,] {
{0.2321119684f,0,0,0,0,0,0,0,0},
{0,0.00086436f,0,0,0,0,0,0,0},
{0,0,0.22648081f,0,0,0,0,0,0},
{0,0,0,0.00002701125f,0,0,0,0,0},
{0,0,0,0,0.000000059536f,0,0,0,0},
{0,0,0,0,0,0.000001946025f,0,0,0},
{0,0,0,0,0,0,0.0000008441015625f,0,0},
{0,0,0,0,0,0,0,0.0000000018605f,0},
{0,0,0,0,0,0,0,0,0.00000006081328125f } });
//veryfied
Matrix startetrans = Matrix.Build.DenseOfArray(new double[,] {
{1,0,0,0,0,0,0,0,0},
{0,1,0,0,0,0,0,0,0},
{0,0,1,0,0,0,0,0,0},
{0,0,0,1,0,0,0,0,0},
{0,0,0,0,1,0,0,0,0},
{0,0,0,0,0,1,0,0,0},
{0,0,0,0,0,0,1,0,0},
{0,0,0,0,0,0,0,1,0},
{0,0,0,0,0,0,0,0,1 } });
kalman.Predict(startetrans, kalman.Cov);
kalman.Update(Meassurement, Noise, covariance);
x1 += kalman.State.At(0, 0);
y1 += kalman.State.At(1, 0);
z1 += kalman.State.At(2, 0);
Console.WriteLine(x1 +","+ y1 +"," +z1);
t2 = time;
x = x1;
y = y1;
z = z1;
}
}
}
}
}