Learning the Extended Kalman Filter
An implementation of Extended Kalman Filter for nonlinear state estimation.
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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.
Cite As
Yi Cao (2026). Learning the Extended Kalman Filter (https://nl.mathworks.com/matlabcentral/fileexchange/18189-learning-the-extended-kalman-filter), MATLAB Central File Exchange. Retrieved .
Acknowledgements
Inspired by: Learning the Kalman Filter
Inspired: Learning the Unscented Kalman Filter, Unconstrained Optimization using the Extended Kalman Filter, Neural Network training using the Extended Kalman Filter
General Information
- Version 1.0.0.0 (2.1 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0.0.0 | Update example with block-comment lines |
