Correction of Kalman filter in the presence of outlier

Author

Dong, Yan ; Hongyue, Zhang

Author_Institution

Dept. of Autom. Control, Beijing Univ. of Aeronaut. & Astronaut., China

fYear

1994

fDate

5-9 Dec 1994

Firstpage

29

Lastpage

33

Abstract

In this paper, a new method of detecting outlier in data is proposed. The new method is based on the identification of the ARMA model of system output. The outlier can be detected by a detection function. The recursive extended least-squares (RELS) method is used to identify the ARMA model of system output. Since the method is very sensitive to changes of coefficients of the ARMA model, an outlier can be detected quickly. Because the performance of Kalman filter will be deteriorated by the outlier, therefore, after the detection of the outlier, the residual of the Kalman filter is smoothed. Using this correction, the performance of the Kalman filter is improved. As an example of application, a simulation of guidance for semi-active radar homing missile is conducted. The result of the simulation proves that the outlier can be detected correctly, and the correction of Kalman filter is efficient and practical

Keywords

Kalman filters; autoregressive moving average processes; filtering theory; identification; least squares approximations; missile guidance; ARMA model; Kalman filter; identification; outlier detection; radar homing missile; recursive extended least-squares; Extraterrestrial measurements; Filtering; Kalman filters; Missiles; Noise measurement; Polynomials; Radar applications; Radar detection; Statistics; Steady-state;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Technology, 1994., Proceedings of the IEEE International Conference on

Conference_Location

Guangzhou

Print_ISBN

0-7803-1978-8

Type

conf

DOI

10.1109/ICIT.1994.467173

Filename

467173