White noise theory of robust nonlinear filtering with correlated state and observation noises

Arunabha Bagchi; Rajeeva Karandikar

White noise theory of robust nonlinear filtering with correlated state and observation noises

Arunabha Bagchi, Rajeeva Karandikar

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

In the direct white noise theory of nonlinear filtering, the state process is still modeled as a Markov process satisfying an Ito stochastic differential equation, while a finitely additive white noise is used to model the observation noise. In the present work, this asymmetry is removed by modeling the state process as the solution of a (stochastic) differential equation with a finitely additive white noise as the input. This makes it possible to introduce correlation between the state and observation noise, and to obtain robust nonlinear filtering equations in the correlated noise case

Original language	Undefined
Title of host publication	31st IEEE Conference on Decision and Control
Place of Publication	Tucson, Arizona
Publisher	IEEE
Pages	1245-1246
Number of pages	0
Publication status	Published - 16 Dec 1992
Event	31st IEEE Conference on Decision and Control, CDC 1992 - Westin La Paloma, Tucson, United States Duration: 16 Dec 1992 → 18 Dec 1992 Conference number: 31

Publication series

Name
Publisher	IEEE
Volume	1

Conference

Conference	31st IEEE Conference on Decision and Control, CDC 1992
Abbreviated title	CDC
Country	United States
City	Tucson
Period	16/12/92 → 18/12/92

Keywords

IR-30898
METIS-141539

Access to Document

00371516
open

Cite this

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title = "White noise theory of robust nonlinear filtering with correlated state and observation noises",

abstract = "In the direct white noise theory of nonlinear filtering, the state process is still modeled as a Markov process satisfying an Ito stochastic differential equation, while a finitely additive white noise is used to model the observation noise. In the present work, this asymmetry is removed by modeling the state process as the solution of a (stochastic) differential equation with a finitely additive white noise as the input. This makes it possible to introduce correlation between the state and observation noise, and to obtain robust nonlinear filtering equations in the correlated noise case",

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N2 - In the direct white noise theory of nonlinear filtering, the state process is still modeled as a Markov process satisfying an Ito stochastic differential equation, while a finitely additive white noise is used to model the observation noise. In the present work, this asymmetry is removed by modeling the state process as the solution of a (stochastic) differential equation with a finitely additive white noise as the input. This makes it possible to introduce correlation between the state and observation noise, and to obtain robust nonlinear filtering equations in the correlated noise case

AB - In the direct white noise theory of nonlinear filtering, the state process is still modeled as a Markov process satisfying an Ito stochastic differential equation, while a finitely additive white noise is used to model the observation noise. In the present work, this asymmetry is removed by modeling the state process as the solution of a (stochastic) differential equation with a finitely additive white noise as the input. This makes it possible to introduce correlation between the state and observation noise, and to obtain robust nonlinear filtering equations in the correlated noise case

KW - IR-30898

KW - METIS-141539

M3 - Conference contribution

SP - 1245

EP - 1246

BT - 31st IEEE Conference on Decision and Control

PB - IEEE

CY - Tucson, Arizona

ER -