A quantified approach of predicting suitability of using the unscented Kalman filter in a non-linear application
From MaRDI portal
Publication:2208560
DOI10.1016/J.AUTOMATICA.2020.109241zbMath1451.93386OpenAlexW3088487308MaRDI QIDQ2208560
Andrew G. Dempster, Li Qiao, Sanat K. Biswas
Publication date: 3 November 2020
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.automatica.2020.109241
trackingnonlinearityextended Kalman filterestimation theorynonlinear observer and filter designunscented Kalman filter
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Stochastic stability of the unscented Kalman filter with intermittent observations
- Estimation of the state of a nonlinear process in the presence of nongaussian noise and disturbances
- Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements
- Performance evaluation of UKF-based nonlinear filtering
- Foundations of feedback theory for nonlinear dynamical systems
- Nonlinear controllability and observability
- Optimal robustness in the gap metric
- von Karman Lecture: Adventures on the Interface of Dynamics and Control
- Stochastic stability of the discrete-time extended Kalman filter
- A new method for the nonlinear transformation of means and covariances in filters and estimators
- Measure of Nonlinearity for Estimation
- The gap metric: Robustness of stabilization of feedback systems
- On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems
- A Novel a Priori State Computation Strategy for the Unscented Kalman Filter to Improve Computational Efficiency
- Unscented Kalman Filter: Aspects and Adaptive Setting of Scaling Parameter
- New developments in state estimation for nonlinear systems
This page was built for publication: A quantified approach of predicting suitability of using the unscented Kalman filter in a non-linear application
