RURUF

The RURUF class implements the randomized unscented recursive update filter.

Configuration

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

Set the linear factors to determine the number of samples used for state prediction and measurement update, i.e., the number of samples, with the setNumSamplesFactors() 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

  • Jindřich Dunı́k, Ondřej Straka, and Miroslav Šimandl, “The Development of a Randomised Unscented Kalman Filter,” in Proceedings of the 18th IFAC World Congress (IFAC 2011), Milano, Italy, Aug. 2011, pp. 8–13.

  • 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.