A Rare-Update Sigma-Point Kalman Filter as Parameter Estimator

J. Fox and H. Janocha (Germany)


identification, estimation, sigma-point Kalman filter, iner tial navigation


Extended Kalman Filters (EKF) have widely been used as state estimators in non-linear dynamic systems. In recent years, a new variant of Kalman filtering has emerged. So called sigma-point Kalman filters (SPKF) exhibit a better accuracy and require less analytical calculations during the design than conventional Kalman filters. As has been done with EKFs, SPKFs can be used to estimate parameters of a system as well. In this work, it will be shown why the ap plication of a SPKF is highly advantageous compared to the EKF in systems where only few update measurements are available while the inputs occur at high frequencies. The calibration of an inertial measurement unit serves as an ex ample for the parameter estimation.

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