PGF
The PGF class implements the progressive Gaussian filter.
Configuration
- Configure the number of samples used for state prediction and measurement update
Set the number of samples used for state prediction and measurement update with the setNumSamples() and setNumSamplesByFactors() methods.
- Set the maximum number of allowed progression steps
Set the maximum number of allowed progression steps per measurement update with the setMaxNumProgSteps() method.
- Use a semi-analytic measurement update
Enable a semi-analytic measurement update if a measurement model does not require all state variables with the setStateDecompDim() method.
- Enable post-processing of the predicted state estimate
Set a post-processing method for the state prediction with the setPredictionPostProcessing() method.
- Enable post-processing of the updated state estimate
Set a post-processing method for the measurement update with the setUpdatePostProcessing() method.
Supported Models
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System Models |
Measurement Models |
Literature
Jannik Steinbring and Uwe D. Hanebeck, “Progressive Gaussian Filtering Using Explicit Likelihoods,” in Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), Salamanca, Spain, Jul. 2014.
Jannik Steinbring and Uwe D. Hanebeck, “GPU-Accelerated Progressive Gaussian Filtering with Applications to Extended Object Tracking,” in Proceedings of the 18th International Conference on Information Fusion (Fusion 2015), Washington D.C., USA, Jul. 2015.
Jannik Steinbring, Antonio Zea, and Uwe D. Hanebeck, “Semi-Analytic Progressive Gaussian Filtering,” in Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2016), Baden-Baden, Germany, Sep. 2016.