SHERP

선형화 오차에 강인한 확장칼만필터
An Extended Kalman Filter Robust to Linearization Error

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Authors
홍현수; 박찬국; 이장규
Issue Date
2006-02
Publisher
제어로봇시스템학회 = Institute of Control, Robotics and Systems
Citation
제어·자동화·시스템공학 논문지, 제12권, 제2호, pp. 93-100, 2. 2006
Keywords
extended Kalman filterrobust nonlinear filterlinearization error
Abstract
In this paper, a new-type Extended Kalman Filter (EKF) is proposed as a robust nonlinear filter for a stochastic nonlinear
system. The original EKF is widely used for various nonlinear system applications. But it is fragile to its estimation errors because they give rise to linearization errors that affect the system model as the modeling errors. The linearization errors are nonlinear functions of the estimation errors therefore it is very difficult to obtain the accurate error covariance of the EKF using the linear form. The inaccurately estimated error covariance hinders the EKF from being a sub-optimal estimator. The proposed filter tries to obtain the upper bound of the error covariance tolerating the uncertainty of the error covariance instead of trying to obtain the accurate one. It treats the linearization errors as uncertain modeling errors that can be handled by the robust linear filtering. In order to be more
robust to the estimation errors than the original EKF, the proposed filter minimizes the upper bound like the robust linear filter that is applied to the linear model with uncertainty. The in-flight alignment problem of the inertial navigation system with GPS position measurements is a good example that the proposed robust filter is applicable to. The simulation results show the efficiency of the proposed filter in the robustness to initial estimation errors of the filter.
ISSN
1225-9845
Language
Korean
URI
http://hdl.handle.net/10371/9660
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Mechanical Aerospace Engineering (기계항공공학부)Journal Papers (저널논문_기계항공공학부)
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