Use of Extended Interval Kalman Filter on Integrated GPS / INS System

GPS/INS integration with uncertain dynamics modelli and uncertain measurement noise is studied. An exten interval Kalman filter is applied to such an uncerta integrated system, since the traditional extended Kalm filter is no longer applicable. The results of a compu simulation indicate that the extended Kalman filteri algorithm is consistent with the traditional extend Kalman filtering scheme, and is also effective for t nonlinear integrated system with uncertainty. Introduction GPS and INS systems have complimentary features can improve performance of navigation and give a hig reliable and accurate navigation solution. The Kalm filter has been applied to the integrated GPS/INS (e Cannon 1991, Da 1997, He et al. 1998). For a Kalm filter to be optimal, a main assumption is that the dynam model and noise statistical parameters should exactly known. This, however, is not the case in most pract situations. For instance, the variances of system noise measurement errors in an integrated system are often accurately known. The parameters in the dynamic mo may be uncertain due to several reasons, such as uns ION GPS '99, 14-17 September 1999, Nashville, TN 190 t t error characteristics of the inertial sensors (Titterton et a 1997), the linearization process of the observation mode changes in environmental conditions, Selective Availability (SA), aircraft manoeuvres, etc. The traditional Kalman filter can not deal with such situations with inaccurate dynamics and statistical parameters. The effects of system uncertainty on the performance of filter have been extensively studied by several authors For instance, Neal (1967), Koussoulas and Leonde (1986) investigated the effects of parameter uncertainty i dynamic models, and Heffes (1966), Sangsuk-Iam (1990 discussed the effects of noise uncertainty; Griffin and Sage (1969) and Lainiotis and Slims (1970) studied the influence of uncertainty in the parameters in both dynamic and statistical models by a sensitivity analysis approach Although robustness for some linear systems has bee investigated in the last two decades, robust Kalman filtering for uncertain linear systems is still an active research topic. Several approaches have been propos using the H ∞ criteria (Nagpal 1991, Xie et al. 1991). A new interval Kalman filter (IKF) developed by Chen (1997) has the same recursive structure as the tradition Kalman filter, escaping the additional computation required for the above H ∞ methods. On the other hand, the IKF has been shown to be better than some existin robust Kalman filtering (Chen et al, 1997). Since the measurement equations are nonlinear and also as t inertial system equations themselves are nonlinear, it i necessary to use an extended interval Kalman filte (EIKF) for the integrated navigation system. Siouris (1997) successfully applied the EIKF to tracking of an incoming ballistic missile system. However, noise uncertainty for the tracking problem has not been studie by using intervals. This paper is focused on the application of the interval system analysis to the integrated GPS/INS with uncertainty. The parameter uncertainties of dynamics model and the noise uncertainties are described by intervals, and the EIKF i established for the nonlinear integrated system.