A reduced-order model for integrated GPS/INS

Author

He, Xiufeng ; Chen, Yongqi ; Iz, H.B.

Author_Institution

Hong Kong Polytech. Univ., Hong Kong

Volume

13

Issue

3

fYear

1998

fDate

3/1/1998 12:00:00 AM

Firstpage

40

Lastpage

45

Abstract

The dominant factor in determining the computation time of the Kalman filter is the dimension n of the model state vector. The number of computations per iteration is on the order of n³. Any reduction in the number of states will benefit directly in terms of increased computation time. In this paper, a high order model in integrated GPS/INS is described first, then a reduced-order model based on the high-order model, is developed. Finally, a faster tracking approach for Kalman filters is discussed. A typical aircraft trajectory is designed for a complex high-dynamic aircraft flight experiment. A Monte Carlo analysis shows that the reduced order model presented in this paper provides satisfactory accuracy for aircraft navigation

Keywords

Global Positioning System; Kalman filters; Monte Carlo methods; aircraft control; aircraft navigation; computational complexity; control system analysis computing; controllability; digital simulation; inertial navigation; observability; reduced order systems; Kalman filter; Monte Carlo analysis; aircraft navigation; aircraft trajectory; complex high-dynamic aircraft flight; computation time; high order model; integrated GPS/INS; iteration; reduced-order model; tracking; Aircraft navigation; Clocks; Differential equations; Global Positioning System; Helium; Monte Carlo methods; Reduced order systems; Satellites; Sparse matrices; Vectors;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems Magazine, IEEE

Publisher

ieee

ISSN

0885-8985

Type

jour

DOI

10.1109/62.659864

Filename

659864