Taylor Moment Expansion for Continuous-Discrete Gaussian Filtering
| Autor: | Toni Karvonen, Simo Särkkä, Zheng Zhao, Roland Hostettler |
|---|---|
| Přispěvatelé: | Sensor Informatics and Medical Technology, Uppsala University, Department of Electrical Engineering and Automation, Aalto-yliopisto, Aalto University |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Gaussian 02 engineering and technology stochastic differential equation Gaussian filtering symbols.namesake Stochastic differential equation 020901 industrial engineering & automation Mathematical model Linearization Taylor series Applied mathematics Continuous-discrete state-space model Electrical and Electronic Engineering Mathematics Taylor moment expansion State-space methods Numerical stability Indium tin oxide Thermal stability Time measurement Computer Science Applications Gaussian filter Moment (mathematics) Nonlinear system Control and Systems Engineering symbols Kalman filtering |
| Popis: | This article is concerned with Gaussian filtering in nonlinear continuous-discrete state-space models. We propose a novel Taylor moment expansion (TME) Gaussian filter, which approximates the moments of the stochastic differential equation with a temporal Taylor expansion. Differently from classical linearization or Ito-Taylor approaches, the Taylor expansion is formed for the moment functions directly and in time variable, not by using a Taylor expansion on the nonlinear functions in the model. We analyze the theoretical properties, including the positive definiteness of the covariance estimate and stability of the TME filter. By numerical experiments, we demonstrate that the proposed TME Gaussian filter significantly outperforms the state-of-the-art methods in terms of estimation accuracy and numerical stability. |
| Databáze: | OpenAIRE |
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