Integration of CKF and UKF for Enhanced State Estimation

This text explores the integration of Cubature Kalman Filter (CKF) and Unscented Kalman Filter (UKF), focusing on how this hybrid approach resolves CKF's limitations in handling state mutations. Both CKF and UKF are prominent state estimation methods, yet each exhibits distinct drawbacks. CKF may struggle with abrupt state changes due to its fixed-point cubature rule implementation, while UKF can face numerical stability issues from its sigma point selection mechanism. The fusion methodology typically involves weighted combination of covariance matrices or adaptive switching between filters based on innovation sequences. In implementation, developers often create hybrid algorithms that employ CKF's spherical-radial cubature points for normal conditions while activating UKF's scaled unscented transformation during detected state transitions. This combined approach leverages CKF's numerical accuracy and UKF's nonlinear handling capabilities, significantly improving robustness in practical applications. Successful case studies demonstrate the fusion method's effectiveness in aerospace navigation and robotic localization systems, establishing it as a viable solution for dynamic state estimation challenges.