DiscreteKalmanFilter(
Matrix x0,
Matrix P0
)
{
KalmanFilter.CheckInitialParameters(x0, P0);
this.x = x0;
this.P = P0;
}
InformationFilter(
Matrix x0,
Matrix P0
)
{
KalmanFilter.CheckInitialParameters(x0, P0);
this.J = P0.Inverse();
this.y = this.J * x0;
this.I = Matrix.Identity(this.y.RowCount, this.y.RowCount);
}
SquareRootFilter(
Matrix x0,
Matrix P0
)
{
KalmanFilter.CheckInitialParameters(x0, P0);
// Decompose the covariance matrix
Matrix[] UDU = UDUDecomposition(P0);
this.U = UDU[0];
this.D = UDU[1];
this.x = x0;
}
InformationFilter(
Matrix state,
Matrix cov,
bool inverted
)
{
KalmanFilter.CheckInitialParameters(state, cov);
if (inverted)
{
this.J = cov;
this.y = state;
}
else
{
this.J = cov.Inverse();
this.y = this.J * state;
}
this.I = Matrix.Identity(state.RowCount, state.RowCount);
}