GHKF

The GHKF class implements the Gauss–Hermite Kalman filter and its iterative version.

Configuration

Configure the number of samples used for state prediction and measurement update

Set the number of quadrature points used for state prediction and measurement update, i.e., the number of samples, with the setNumQuadraturePoints() method.

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

System Models

Measurement Models

Literature

  • Kazufumi Ito and Kaiqi Xiong, “Gaussian Filters for Nonlinear Filtering Problems,” IEEE Transactions on Automatic Control, vol. 45, no. 5, pp. 910–927, May 2000.

  • 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.