S2RUF
The S2RUF class implements the smart sampling recursive update 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.
- Configure the recurive update
Set the number of recursion steps that are performed by a measurement update with the setNumRecursionSteps() method.
- Enable measurement gating
Enable measurement gating with the setMeasGatingThreshold() 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
Literature
Jannik Steinbring, Martin Pander, and Uwe D. Hanebeck, “The Smart Sampling Kalman Filter with Symmetric Samples,” Journal of Advances in Information Fusion, vol. 11, no. 1, pp. 71–90, Jun. 2016.
Jannik Steinbring and Uwe D. Hanebeck, “LRKF Revisited: The Smart Sampling Kalman Filter (S²KF),” Journal of Advances in Information Fusion, vol. 9, no. 2, pp. 106–123, Dec. 2014.
Yulong Huang, Yonggang Zhang, Ning Li, and Lin Zhao, “Design of Sigma-Point Kalman Filter with Recursive Updated Measurement,” Circuits, Systems, and Signal Processing, pp. 1–16, Aug. 2015.