private void initializeKalman(PointF startPoint)
{
var measurementDimension = 2; //just coordinates
var initialState = new ModelState {
Position = startPoint, Velocity = new PointF(0.5f, -2f)
};
var initialStateError = ModelState.GetProcessNoise(0);
kalman = new DiscreteKalmanFilter <ModelState, PointF>(initialState, initialStateError,
measurementDimension /*(position)*/, 0 /*no control*/,
x => ModelState.ToArray(x), x => ModelState.FromArray(x), x => new double[] { x.X, x.Y });
kalman.ProcessNoise = ModelState.GetProcessNoise(2);
kalman.MeasurementNoise = Matrix.Diagonal <double>(kalman.MeasurementVectorDimension, 1);
kalman.MeasurementMatrix = new double[, ] //just pick point coordinates for an observation [2 x 4] (look at ConstantVelocity2DModel)
{
//X, vX, Y, vY (look at ConstantVelocity2DModel)
{ 1, 0, 0, 0 }, //picks X
{ 0, 0, 1, 0 } //picks Y
};
kalman.TransitionMatrix = ModelState.GetTransitionMatrix();
}
private void initializeKalman()
{
float accelNoise = (float)numProcessNoise.Value;
float measurementNoise = (float)numMeasurementNoise.Value;
var measurementDimension = 2; //just coordinates
var initialState = process.GetNoisyState(accelNoise); //assuming we measured process params (noise)
var initialStateError = ModelState.GetProcessNoise(accelNoise);
kalman = new DiscreteKalmanFilter <ModelState, PointF>(initialState, initialStateError,
measurementDimension /*(position)*/, 0 /*no control*/,
x => ModelState.ToArray(x), x => ModelState.FromArray(x), x => new double[] { x.X, x.Y });
kalman.ProcessNoise = ModelState.GetProcessNoise(accelNoise);
kalman.MeasurementNoise = Matrix.Diagonal <double>(kalman.MeasurementVectorDimension, measurementNoise).ElementwisePower(2); //assuming we measured process params (noise) - ^2 => variance
kalman.MeasurementMatrix = new double[, ] //just pick point coordinates for an observation [2 x 4] (look at ConstantVelocity2DModel)
{
//X, vX, Y, vY (look at ConstantVelocity2DModel)
{ 1, 0, 0, 0 }, //picks X
{ 0, 0, 1, 0 } //picks Y
};
kalman.TransitionMatrix = ModelState.GetTransitionMatrix(ConstantVelocityProcess.TimeInterval);
}
private void initializeKalman(PointF startPoint)
{
var measurementDimension = 2; //just coordinates
var initialState = new ModelState {
Position = startPoint, Velocity = new PointF(0.2f, -1.5f)
};
var initialStateError = ModelState.GetProcessNoise(10);
kalman = new DiscreteKalmanFilter <ModelState, PointF>(initialState, initialStateError,
measurementDimension /*(position)*/, 0 /*no control*/,
x => ModelState.ToArray(x), x => ModelState.FromArray(x), x => new double[] { x.X, x.Y });
kalman.ProcessNoise = ModelState.GetProcessNoise(1);
kalman.MeasurementNoise = Matrix.Diagonal <double>(kalman.MeasurementVectorDimension, 10000.0);
kalman.MeasurementMatrix = ModelState.GetPositionMeasurementMatrix();
kalman.TransitionMatrix = ModelState.GetTransitionMatrix();
}
public static ConstantVelocity2DModel Evaluate(ConstantVelocity2DModel state, double[,] transitionMat)
{
var stateVector = state.ToArray().Multiply(transitionMat);
return ConstantVelocity2DModel.FromArray(stateVector);
}
public static ConstantVelocity2DModel Evaluate(ConstantVelocity2DModel state, double[,] transitionMat)
{
var stateVector = state.ToArray().Multiply(transitionMat);
return(ConstantVelocity2DModel.FromArray(stateVector));
}
