A real-time prognostic method for the drift errors in the inertial navigation system by a nonlinear random-coefficient regression model

Inertial navigation systems have been widely used in both civilian and military systems because of their autonomous navigation capability. Nevertheless, due to its autonomous characteristics, the navigation precision of an inertial navigation system is heavily influenced by its drift errors, which results from the performance degradation of the system in use. One of the most effective means of eliminating such adverse effects is to predict the drift error values in advance, and compensate for them subsequently. It is therefore significantly important to accurately predict the degrading trend of the drift errors of an inertial navigation system. We propose a novel degradation modeling method based on a nonlinear random-coefficient regression model to predict the drift errors. The parameters of the model are dynamically updated by the expectation maximization algorithm, in conjunction with the Bayesian inference method at the time when a new drift error data is observed. In doing this, the degrading trend of the drift errors can be predicted in real time. Finally, a batch of drift error data of an inertial navigation system is used to validate the feasibility and effectiveness of the developed prognostic method. (C) 2014 IAA. Published by Elsevier Ltd. All rights reserved.