Class DynamicOutlierFilter<T extends ObservedMeasurement<T>>

java.lang.Object
org.orekit.estimation.measurements.modifiers.OutlierFilter<T>
org.orekit.estimation.measurements.modifiers.DynamicOutlierFilter<T>
Type Parameters:
T - the type of the measurement
All Implemented Interfaces:
EstimationModifier<T>, ParameterDriversProvider

public class DynamicOutlierFilter<T extends ObservedMeasurement<T>> extends OutlierFilter<T>
Modifier that sets estimated measurement weight to 0 if residual is too far from expected domain. The "dynamic" aspect comes from the fact that the value of sigma can be changed on demand. This is mainly used when searching for outliers in Kalman filters' prediction phase. The value of sigma is then set to the square root of the diagonal of the matrix (H.Ppred.Ht+R) Note that in the case of the Kalman filter we use the "iteration" word to represent the number of measurements processed by the filter so far.
Since:
9.2
Author:
Luc Maisonobe
  • Constructor Details

    • DynamicOutlierFilter

      public DynamicOutlierFilter(int warmup, double maxSigma)
      Simple constructor.
      Parameters:
      warmup - number of iterations before with filter is not applied
      maxSigma - detection limit for outlier
  • Method Details