Ensemble Kalman Filter for Continuous-Discrete State-Space Models
- Resource Type
- Conference
- Authors
- Murata, Masaya; Kawano, Isao; Inoue, Koichi
- Source
- 2021 60th IEEE Conference on Decision and Control (CDC) Decision and Control (CDC), 2021 60th IEEE Conference on. :6608-6613 Dec, 2021
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Satellites
Filtering
Conferences
Probability density function
Benchmark testing
Prediction algorithms
Numerical simulation
- Language
- ISSN
- 2576-2370
The ensemble Kalman filter (EnKF) is well-established for discrete state-space models. In this paper, we provide the methodology of applying the EnKF to continuous-discrete (CD) state-space models. The proposed CD EnKF algorithm is a bank of the CD extended Kalman filters for the time update. Then, the observation update is formulated using the Gaussian-sum distributed predicted state probability density function (PDF). We also provide the observation update based on the Dirac’s delta mixture predicted state PDF. The numerical simulation using a benchmark filtering problem called the satellite reentry is conducted to investigate the performance of the CD EnKFs. The performance comparison with the EnKF applied to the discretized model is also made.