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
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;
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
DOI :
10.1109/MFI.1994.398480