An Error Correction Approach based on AR model and RLS for Inertial Navigation System

Abstract

In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems (INS), an error correction approach based on auto regressive (AR) model and recursive least squares (RLS) is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, a modified recursive least square is introduced, which can quickly estimate the model parameters before using Kalman filter to real-time filtering. Finally, FOG signal under different motion conditions are employed to validate the effectiveness of the proposed approach. The analysis results show that proposed approach can reduce the random drift error of FOG effectively. In addition, Navigation accuracy can be increased by 32% when inertial navigation lasts for 500s.