Nonlinear Estimation With State-Dependent Gaussian Observation Noise

Author

Spinello, Davide ; Stilwell, Daniel J.

Author_Institution

Dept. of Mech. Eng., Univ. of Ottawa, Ottawa, ON, Canada

Volume

55

Issue

6

fYear

2010

fDate

6/1/2010 12:00:00 AM

Firstpage

1358

Lastpage

1366

Abstract

We consider the problem of estimating the state of a system when measurement noise is a function of the system´s state. We propose generalizations of the extended Kalman filter and the iterated extended Kalman filter that can be utilized when the state estimate distribution is approximately Gaussian. The state estimate is computed by an iterative root-searching method that maximizes a maximum likelihood function. The new filter allows for the consistent treatment of a class of control problem involving nonlinear estimation from measurements with state-dependent noise. The effectiveness of the estimation algorithm is illustrated for a control problem with a mobile bearing-only sensor.

Keywords

Gaussian noise; Kalman filters; iterative methods; maximum likelihood estimation; noise measurement; nonlinear estimation; control problem; extended Kalman filter; iterative root searching method; maximum likelihood function; measurement noise; mobile bearing only sensor; nonlinear estimation; state dependent Gaussian observation noise; state dependent noise; state estimation distribution; Filters; Gaussian noise; Information filtering; Information filters; Iterative methods; Maximum likelihood estimation; Motion control; Noise measurement; Nonlinear filters; Postal services; State estimation; Stochastic processes; Target tracking; Wireless sensor networks; Extended Kalman filter; mobile sensors; nonlinear estimation; state-dependent noise;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/TAC.2010.2042006

Filename

5404342