Kalman Filtering with Uncertain Noise Covariances

Srikiran Kosanam; Daniel J. Simon

Kalman Filtering with Uncertain Noise Covariances

Cleveland State University

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper the robustness of Kalman filtering against uncertainties in process and measurement noise covariances is discussed. It is shown that a standard Kalman filter may not be robust enough if the process and measurement noise covariances are changed. A new filter is proposed which addresses the uncertainties in process and measurement noise covariances and gives better results than the standard Kalman filter. This new filter is used in simulation to estimate the health parameters of an aircraft gas turbine engine.

Original language	American English
Journal	Intelligent Systems and Control
State	Published - Aug 1 2004

Keywords

Kalman filtering
Robust filtering
Parameter estimation and Ricati equation

Disciplines

Electrical and Computer Engineering

Access to Document

Kalman Filtering with Uncertain Noise CovariancesFinal published version, 931 KBLicense: CC BY

Cite this

@article{c5df055b249f4c67a266f2a190c76a94,

title = "Kalman Filtering with Uncertain Noise Covariances",

abstract = " In this paper the robustness of Kalman filtering against uncertainties in process and measurement noise covariances is discussed. It is shown that a standard Kalman filter may not be robust enough if the process and measurement noise covariances are changed. A new filter is proposed which addresses the uncertainties in process and measurement noise covariances and gives better results than the standard Kalman filter. This new filter is used in simulation to estimate the health parameters of an aircraft gas turbine engine.",

keywords = "Kalman filtering, Robust filtering, Parameter estimation and Ricati equation",

author = "Srikiran Kosanam and Simon, \{Daniel J.\}",

note = "S. Kosanam and D. Simon. (2004). Kalman Filtering with Uncertain Noise Covariances, Intelligent Systems and Control, Honolulu, HI, 375-379.",

year = "2004",

month = aug,

day = "1",

language = "American English",

journal = "Intelligent Systems and Control",

}

TY - JOUR

T1 - Kalman Filtering with Uncertain Noise Covariances

AU - Kosanam, Srikiran

AU - Simon, Daniel J.

N1 - S. Kosanam and D. Simon. (2004). Kalman Filtering with Uncertain Noise Covariances, Intelligent Systems and Control, Honolulu, HI, 375-379.

PY - 2004/8/1

Y1 - 2004/8/1

N2 - In this paper the robustness of Kalman filtering against uncertainties in process and measurement noise covariances is discussed. It is shown that a standard Kalman filter may not be robust enough if the process and measurement noise covariances are changed. A new filter is proposed which addresses the uncertainties in process and measurement noise covariances and gives better results than the standard Kalman filter. This new filter is used in simulation to estimate the health parameters of an aircraft gas turbine engine.

AB - In this paper the robustness of Kalman filtering against uncertainties in process and measurement noise covariances is discussed. It is shown that a standard Kalman filter may not be robust enough if the process and measurement noise covariances are changed. A new filter is proposed which addresses the uncertainties in process and measurement noise covariances and gives better results than the standard Kalman filter. This new filter is used in simulation to estimate the health parameters of an aircraft gas turbine engine.

KW - Kalman filtering

KW - Robust filtering

KW - Parameter estimation and Ricati equation

UR - https://engagedscholarship.csuohio.edu/enece_facpub/196

M3 - Article

JO - Intelligent Systems and Control

JF - Intelligent Systems and Control

ER -

Kalman Filtering with Uncertain Noise Covariances

Abstract

Keywords

Disciplines

Access to Document

Other files and links

Cite this