S2KF
The S2KF class implements the smart sampling Kalman filter and its iterative version.
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.
- Enable the iterative measurement update
Enable the iterative measurement update with the setMaxNumIterations() method. Additionally, you can check for convergence, i.e., if no further iterations are required, with the setConvergenceCheck() 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.
Ángel F. Garcı́a-Fernández, Lennart Svensson, Mark Morelande, and Simo Särkkä, “Posterior Linearisation Filter: Principles and Implementation Using Sigma Points,” IEEE Transactions on Signal Processing, vol. 63, no. 20, pp. 5561–5573, Oct. 2015.