public KalmanFilter(int Rows, int Columns, double[,] x, Covariance CM)
{
this.Sv = 1;
this.RowsCount = Rows;
this.ColumnsCount = Columns;
this.a_tt = new double[RowsCount, ColumnsCount];
this.A_tt = new double[RowsCount, ColumnsCount];
this.OutputSignal = new double[RowsCount, ColumnsCount];
this.Y2 = new double[RowsCount, ColumnsCount];
this.E = new double[RowsCount, ColumnsCount];
this.InputSignal = x;
CovMatr = CM;
Noize = new NormalDistributionSignal(0, Math.Sqrt(Math.PI * 2 * Sv / CovMatr.tau));
Noize.SignalArrayLength = ColumnsCount;
Noize.GenerateSignal();
}
public FormingFilter(int Rows, int Columns, double tau, double ksi, double sigma, double teta, double fi)
{
this.tau = tau;
this.Sv = 1;
this.ColumnsCount = Columns;
this.RowsCount = Rows;
this.OutputSignal = new double[RowsCount, ColumnsCount];
this.H = new double[RowsCount, ColumnsCount];
this.teta = teta;
this.sigma = sigma;
this.ksi = ksi;
this.fi = fi;
InitialSignal = new NormalDistributionSignal(0, Math.Sqrt(Math.PI * 2 * Sv / tau));
InitialSignal.SignalArrayLength = ColumnsCount;
InitialSignal.GenerateSignal();
this.InputSignal = new double[1, ColumnsCount];
for (int i = 0; i < InitialSignal.SignalArrayLength; i++)
{
this.InputSignal[0, i] = InitialSignal.SignalArray[i];
}
}
