/// <summary>
/// Creates a new Discrete Time Kalman Filter with the given values for
/// the initial state and the covariance of that state.
/// </summary>
/// <param name="x0">The best estimate of the initial state of the estimate.</param>
/// <param name="P0">The covariance of the initial state estimate. If unsure
/// about initial state, set to a large value</param>
public DiscreteKalmanFilter(Matrix <double> x0, Matrix <double> P0)
{
KalmanFilter.CheckInitialParameters(x0, P0);
x = x0;
P = P0;
}
/// <summary>
/// Creates an Information filter with the given initial state.
/// </summary>
/// <param name="x0">Initial estimate of state variables.</param>
/// <param name="P0">Covariance of state variable estimates.</param>
public InformationFilter(Matrix <double> x0, Matrix <double> P0)
{
KalmanFilter.CheckInitialParameters(x0, P0);
J = P0.Inverse();
y = J * x0;
I = Matrix <double> .Build.DenseIdentity(y.RowCount, y.RowCount);
}
/// <summary>
/// Creates a square root filter with given initial state.
/// </summary>
/// <param name="x0">Initial state estimate.</param>
/// <param name="P0">Covariance of initial state estimate.</param>
public SquareRootFilter(Matrix <double> x0, Matrix <double> P0)
{
KalmanFilter.CheckInitialParameters(x0, P0);
// Decompose the covariance matrix
Matrix <double>[] UDU = UDUDecomposition(P0);
U = UDU[0];
D = UDU[1];
x = x0;
}
/// <summary>
/// Creates an Information filter specifying whether the covariance and state
/// have been 'inverted'.
/// </summary>
/// <param name="state">The initial estimate of the state of the system.</param>
/// <param name="cov">The covariance of the initial state estimate.</param>
/// <param name="inverted">Has covariance/state been converted to information
/// filter form?</param>
/// <remarks>This behaves the same as other constructors if the given boolean is false.
/// Otherwise, in relation to the given state/covariance should satisfy:<BR></BR>
/// <C>cov = J = P0 ^ -1, state = y = J * x0.</C></remarks>
public InformationFilter(Matrix <double> state, Matrix <double> cov, bool inverted)
{
KalmanFilter.CheckInitialParameters(state, cov);
if (inverted)
{
J = cov;
y = state;
}
else
{
J = cov.Inverse();
y = J * state;
}
I = Matrix <double> .Build.DenseIdentity(state.RowCount, state.RowCount);
}