Abstract:
An alternative method of analyzing the convergence of the discrete-time extended Kalman filter used as a deterministic nonlinear observer is shown. This work utilizes Lya...Show MoreMetadata
Abstract:
An alternative method of analyzing the convergence of the discrete-time extended Kalman filter used as a deterministic nonlinear observer is shown. This work utilizes Lyapunov analysis techniques on the extended Kalman filter in the direct form to do convergence analysis with reduced assumptions on the system matrix.
Published in: SoutheastCon 2018
Date of Conference: 19-22 April 2018
Date Added to IEEE Xplore: 04 October 2018
ISBN Information:
ISSN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Kalman Filter ,
- Extended Kalman Filter ,
- Nonlinear Observer ,
- System Matrix ,
- Lyapunov Analysis ,
- Nonlinear Systems ,
- First Approximation ,
- Taylor Series ,
- Observation Error ,
- Magnitude Of Error ,
- Minimum Estimate ,
- Error Dynamics ,
- Convergence Of Error ,
- Kalman Gain ,
- Real Elements ,
- Theoretical Bound ,
- Discrete-time Nonlinear Systems ,
- Schur Complement
- Author Keywords
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Kalman Filter ,
- Extended Kalman Filter ,
- Nonlinear Observer ,
- System Matrix ,
- Lyapunov Analysis ,
- Nonlinear Systems ,
- First Approximation ,
- Taylor Series ,
- Observation Error ,
- Magnitude Of Error ,
- Minimum Estimate ,
- Error Dynamics ,
- Convergence Of Error ,
- Kalman Gain ,
- Real Elements ,
- Theoretical Bound ,
- Discrete-time Nonlinear Systems ,
- Schur Complement
- Author Keywords
