The well-known Kalman รฟlter is the optimal รฟlter for a linear Gaussian state-space model. Furthermore, the Kalman รฟlter is one of the few known รฟnite-dimensional รฟlters. In search of other discrete-time รฟnitedimensional รฟlters, this paper derives รฟlters for general linear exponential state-space mod
Exponential stability of discrete-time filters for bounded observation noise
โ Scribed by A. Budhiraja; D. Ocone
- Publisher
- Elsevier Science
- Year
- 1997
- Tongue
- English
- Weight
- 485 KB
- Volume
- 30
- Category
- Article
- ISSN
- 0167-6911
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โฆ Synopsis
This paper proves exponential asymptotic stability of discrete-time filters for the estimation of solutions to stochastic difference equations, when the observation noise is bounded. No assumption is made on the ergodicity of the signal. The proof uses the Hilbert projective metric, introduced into filter stability analysis by Atar and Zeitouni [1,2]. It is shown that when the signal noise is sufficiently regular, boundedness of the observation noise implies that the filter update operation is, on average, a strict contraction with respect to the Hilbert metric. Asymptotic stability then follows.
๐ SIMILAR VOLUMES
The averaging method for nonlinear di erential equations with a fast time variable is extended to systems with deterministic bounded noise. The averaged system is used to present a su cient condition for the uniform exponential stability of the original system. The method of proof is based on a dire