Adaptive Two-Stage EKF for INS-GPS Loosely Coupled System with Unknown Fault Bias

Abstract

This paper proposes an adaptive two-stage extended Kalman filter (ATEKF) for estimation of unknown fault bias in an INS-GPS loosely coupled system. The Kalman filtering technique requires complete specifications of both dynamical and statistical model parameters of the system. However, in a number of practical situations, these models may contain parameters, which may deviate from their nominal values by unknown random bias. This unknown random bias may seriously degrade the performance of the filter or cause a divergence of the filter. The two-stage extended Kalman filter (TEKF), which considers this problem in nonlinear system, has received considerable attention for a long time. The TEKF suggested until now assumes that the information of a random bias is known. But the information of a random bias is unknown or partially known in general. To solve this problem, this paper firstly proposes a new adaptive fading extended Kalman filter (AFEKF) that can be used for nonlinear system with incomplete information. Secondly, it proposes the ATEKF that can estimate unknown random bias by using the AFEKF. The proposed ATEKF is more effective than the TEKF for the estimation of the unknown random bias. The ATEKF is applied to the INS-GPS loosely coupled system with unknown fault bias.