URUF

The URUF class implements the unscented recursive update filter.

Configuration

Configure the samples for state prediction and measurement update

Set the sample scaling factors, i.e., spread and weights of the samples, used for state prediction and measurement update with the setSampleScalings() method.

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

System Models

Measurement Models

Literature

  • Simon J. Julier and Jeffrey K. Uhlmann, “Unscented Filtering and Nonlinear Estimation,” in Proceedings of the IEEE, vol. 92, Mar. 2004, pp. 401–422.

  • 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.