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Note de l’éditeur : This file was selected as MATLAB Central Pick of the Week
This is a tutorial on nonlinear extended Kalman filter (EKF). It uses the standard EKF fomulation to achieve nonlinear state estimation. Inside, it uses the complex step Jacobian to linearize the nonlinear dynamic system. The linearized matrices are then used in the Kalman filter calculation.
The complex step differentiation seems improving the EKF performance particularly in accuracy such that the optimization and NN training through the EKF are better than through the UKF (unscented Kalman filter, http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=18217&objectType=FILE). Other complex step differentiation tools include the CSD Hessian available at http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=18177&objectType=FILE.
Citation pour cette source
Yi Cao (2026). Learning the Extended Kalman Filter (https://fr.mathworks.com/matlabcentral/fileexchange/18189-learning-the-extended-kalman-filter), MATLAB Central File Exchange. Extrait(e) le .
Remerciements
Inspiré par : Learning the Kalman Filter
A inspiré : Learning the Unscented Kalman Filter, Unconstrained Optimization using the Extended Kalman Filter, Neural Network training using the Extended Kalman Filter
Informations générales
- Version 1.0.0.0 (2,1 ko)
Compatibilité avec les versions de MATLAB
- Compatible avec toutes les versions
Plateformes compatibles
- Windows
- macOS
- Linux
| Version | Publié le | Notes de version | Action |
|---|---|---|---|
| 1.0.0.0 | Update example with block-comment lines |
