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Nonlinear filtering with signal dependent observation noise

Dublin Core		PKP Metadata Items	Metadata for this Document
1.	Title	Title of document	Nonlinear filtering with signal dependent observation noise
2.	Creator	Author's name, affiliation, country	Dan Crisan; Imperial College
2.	Creator	Author's name, affiliation, country	Michael A. Kouritzin; University of Alberta
2.	Creator	Author's name, affiliation, country	Jie Xiong; University of Kentucky
3.	Subject	Discipline(s)
3.	Subject	Keyword(s)	Nonlinear Filtering, Ornstein Uhlenbeck Noise, Signal-
3.	Subject	Subject classification	Primary: 60G35, Secondary: 60G30
4.	Description	Abstract	The paper studies the filtering problem for a non-classical frame- work: we assume that the observation equation is driven by a signal dependent noise. We show that the support of the conditional distri- bution of the signal is on the corresponding level set of the derivative of the quadratic variation process. Depending on the intrinsic dimension of the noise, we distinguish two cases: In the first case, the conditional distribution has discrete support and we deduce an explicit represen- tation for the conditional distribution. In the second case, the filtering problem is equivalent to a classical one defined on a manifold and we deduce the evolution equation of the conditional distribution. The re- sults are applied to the filtering problem where the observation noise is an Ornstein-Uhlenbeck process.
5.	Publisher	Organizing agency, location
6.	Contributor	Sponsor(s)	NSERC, NSF
7.	Date	(YYYY-MM-DD)	2009-09-02
8.	Type	Status & genre	Peer-reviewed Article
8.	Type	Type
9.	Format	File format	PDF
10.	Identifier	Uniform Resource Identifier	http://ejp.ejpecp.org/article/view/687
10.	Identifier	Digital Object Identifier	10.1214/EJP.v14-687
11.	Source	Journal/conference title; vol., no. (year)	Electronic Journal of Probability; Vol 14
12.	Language	English=en
14.	Coverage	Geo-spatial location, chronological period, research sample (gender, age, etc.)
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