Abstract
Estimation of states in stochastic differential equations with state dependent diffusion is known to be difficult. Previous research recommend the higher order extended Kalman filter or the Lamperti transform method for this case. This paper shows that a new developed method, based on the unscented Kalman filter, is superior for two simulated stochastic differential equation systems.
| Original language | English |
|---|---|
| Book series | IFAC-PapersOnLine |
| Volume | 56 |
| Issue number | 2 |
| Pages (from-to) | 5943-5950 |
| Number of pages | 8 |
| ISSN | 1474-6670 |
| DOIs | |
| Publication status | Published - 1 Jul 2023 |
| Event | 22nd IFAC World Congress - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023 |
Conference
| Conference | 22nd IFAC World Congress |
|---|---|
| Country/Territory | Japan |
| City | Yokohama |
| Period | 09/07/2023 → 14/07/2023 |
| Sponsor | Azbil Corporation, et al., Fujita Corporation, Hitachi, Ltd., Kumagai Gumi Co., Ltd., The Society of Instrument and Control Engineers (SICE) |
Bibliographical note
Publisher Copyright:Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Keywords
- Continuous time system estimation
- Estimation and filtering
- Higher order Kalman filter
- Lamperti transform
- Nonlinear system identification
- Stochastic differential equations
- Stochastic system identification
- Unscented Kalman filter