BatchEstimatorInv

Navigation Requires Use of Fixed Step Numerical Integration

GMAT navigation requires use of fixed stepped propagation. The BatchEstimatorInv resource has a Propagator field containing the name of the Propagator resource that will be used during the estimation process. As shown in the Note below, there are some hard restrictions on the choice of error control specified for the ForceModel resource associated with your propagator.

Behavior of Convergence Criteria

GMAT has four input fields, RelativeTol, AbsoluteTol, MaximumIterations, and MaxConsecutiveDivergences that are used to determine if the estimator has converged after each new iteration. Associated with these input fields are the two convergence tests shown below:

Absolute Weighted RMS convergence criteria

        Weighted RMScurrent <= AbsoluteTol

Relative Weighted Root Mean Square (RMS) convergence criteria

        |RMSP – RMSB|/ RMSB <= RelativeTol

where

        RMSB = smallest Weighted RMS achieved during the current and previous iterations

        RMSP = predicted Weighted RMS of next iteration

Batch estimation is considered to have converged when either or both of the above criteria is met within MaximumIterations iterations or less.

Batch estimation is considered to have diverged when number of consecutive diverging iterations is equal to or greater than MaxConsecutiveDivergences or the number of iterations exceeds MaximumIterations.

Behavior of Outer Loop Sigma Editing (OLSE)

GMAT has four input fields, OLSEMultiplicativeConstant, OLSEAdditiveConstant, OLSEUseRMSP, and OLSEInitialRMSSigma, that are used to 'edit' (i.e., reject or throw away) bad measurement data. There are plans to have both an inner loop and and outer loop iteration editing procedure. Currently, only the outer loop iteration editing procedure is implemented. This editing procedure is done on a per iteration basis. Data that is edited is not used to calculate the state vector estimate for the current iteration but the data is available as a candidate measurement for subsequent iterations. On the first outer loop iteration, data is edited if

        |Weighted Measurement Residual| > OLSEInitialRMSSigma

where the Weighted Measurement Residual for a single given measurement is given by

        (O-C)/NoiseSigma

and where NoiseSigma is the input noise (one sigma) for the measurement type associated with the given measurement. On subsequent outer loop iterations, the data is edited if

        |Weighted Measurement Residual| > OLSEMultiplicativeConstant * RMS + OLSEAdditiveConstant

The editing algorithm above depends upon the user input value of OLSEUseRMSP. If OLSEUseRMSP = True, then RMS = WRMSP where WRMSP is the predicted weighted RMS calculated at the end of the previous iteration. Otherwise, If OLSEUseRMSP = False, then RMS = WRMS where WRMS is the actual weighted RMS calculated at the end of the previous iteration.

Behavior of Freezing Measurement Editing

GMAT has two input fields, FreezeMeasurementEditing and FreezeIteration, that are used to determine if and when to 'freeze' (i.e., no longer change) the selection of measurements which are edited out by the Outer Loop Sigma Editor. Freezing the measurement editing only takes place when FreezeMeasurementEditing is true.

If freezing is enabled, the selection of measurements to edit is locked after the iteration specified by FreezeIteration. If the value of FreezeIteration is 1, the estimator uses the value of OLSEInitialRMSSigma, as defined above, to determine which measurements are used to calculate the first iteration of the state vector deviation vector. Afterwards, the same measurements edited out by the initial RMS sigma filter are edited out for the remainder of the iterations. If the value of FreezeIteration is 2 or greater, the estimator uses the above defined outer loop sigma editing to determine the state vector deviation vector up to the iteration specified by FreezeIteration, at which point whichever measurements are edited out by the outer loop sigma editor stay edited out for the remainder of the iterations. Frozen measurements that are edited out will retain the edit flag the outer loop sigma editor used the iteration they were edited out.

Freezing measurement editing can be useful in situations where a solution takes an excessive number of iterations to converge and latter iterations are only editing a small amount of data. If this is the case, enabling the editing freeze on an appropriate iteration will generally force the solution to converge quickly after reaching the frozen iteration.

Propagator Settings

The BatchEstimatorInv resource has a Propagator field containing the name of the Propagator resource that will be used during the estimation process. The minimum step size, MinStep, of your propagator should always be set to 0.

UseInitialCovariance Restrictions

As mentioned in the Field spec above, if this field is set to true, then the a priori error covariance term is added to the estimation cost function. For the current GMAT release, there are some restrictions on the use of this field as given below.

Interactions

Below is an example of a configured batch estimator instance. In this example, estData is an instance of a TrackingFileSet and ODProp is an instance of Propagator.

Create BatchEstimatorInv bat;

bat.ShowProgress               = true;
bat.Measurements               = {estData} 
bat.AbsoluteTol                = 0.000001;
bat.RelativeTol                = 0.001;
bat.MaximumIterations          = 10;
bat.MaxConsecutiveDivergences  = 3;
bat.Propagator                 = ODProp;
bat.ShowAllResiduals           = On;
bat.OLSEInitialRMSSigma        = 3000;
bat.OLSEMultiplicativeConstant = 3;
bat.OLSEAdditiveConstant       = 0;
bat.InversionAlgorithm         = 'Internal';
bat.EstimationEpochFormat      = 'FromParticipants';
bat.EstimationEpoch            = 'FromParticipants'; 
bat.ReportStyle                = 'Normal';
bat.ReportFile                 = 'BatchEstimator_Report.txt';

BeginMissionSequence;

For a comprehensive example of reading in measurements and running the estimator, see the Chapter 14, Orbit Estimation using DSN Range and Doppler Data tutorial.