EKF2

The EKF2 class implements the second-order extended Kalman filter and its iterative version.

Configuration

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

  • Michael Athans, Richard P. Wishner, and Anthony Bertolini, “Suboptimal State Estimation for Continuous-Time Nonlinear Systems from Discrete Noisy Measurements,” IEEE Transactions on Automatic Control, vol. 13, no. 5, pp. 504–514, Oct. 1968.