Cramer-Rao bounds for discrete-time nonlinear filtering problems

Author

Doerschuk, Peter C.

Author_Institution

Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN, USA

Volume

Issue

fYear

1995

fDate

8/1/1995 12:00:00 AM

Firstpage

1465

Lastpage

1469

Abstract

In this note, a Cramer-Rao bound for the mean squared error that can be achieved with nonlinear observations of a nonlinear pth order autoregressive (AR) process where both the process and observation noise covariances can be state dependent is presented. The major limitation is that the AR process must be driven by an additive white Gaussian noise process that has a full-rank covariance. A numerical example demonstrating the tightness of the bound for a particular problem is included

Keywords

Gaussian noise; autoregressive processes; filtering theory; nonlinear filters; state estimation; Cramer-Rao bounds; additive white Gaussian noise process; discrete-time nonlinear filtering problems; full-rank covariance; mean squared error; nonlinear observations; nonlinear pth order autoregressive process; state dependent noise covariances; Additive noise; Additive white noise; Covariance matrix; Difference equations; Filtering; Gaussian noise; Parameter estimation; Probability density function; State estimation; Stochastic resonance;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.402242

Filename

402242

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=847195