DocumentCode
2670340
Title
Filter design methods of multiple model system
Author
Dong, Yan ; Hongyue, Zhang
Author_Institution
Dept. of Autom. Control, Beijing Univ. of Aeronaut. & Astronaut., China
fYear
1994
fDate
2-5 Oct 1994
Firstpage
59
Lastpage
63
Abstract
In this paper, two filter design methods of multiple model system are proposed. One is the identification of ARMA model, and the other is χ2 test. The identification of ARMA model means the steady state gain matrix of Kalman filter can be identified online via recursive extended least squares method, by comparison of steady-state Kalman filter gain with the Kalman filter gain obtained from possible model, the true gain matrix can be determined by the principle of minimal error norm. The χ2 test method means the true model can be determined by detection of the whiteness of innovations process. The two methods are applied to homing guidance system. The simulation results prove that both methods are effective
Keywords
Kalman filters; autoregressive moving average processes; filtering theory; least squares approximations; matrix algebra; missile guidance; state estimation; χ2 test; ARMA model; Kalman filter; filter design; homing guidance system; identification; innovations process whiteness; minimal error norm; multiple model system; recursive extended least squares method; state estimation; steady state gain matrix; Adaptive filters; Control systems; Design methodology; Equations; Kalman filters; Polynomials; State estimation; Steady-state; Technological innovation; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Multisensor Fusion and Integration for Intelligent Systems, 1994. IEEE International Conference on MFI '94.
Conference_Location
Las Vegas, NV
Print_ISBN
0-7803-2072-7
Type
conf
DOI
10.1109/MFI.1994.398480
Filename
398480
Link To Document