A new robust filtering for a GPS/SINS loosely coupled integration system

Abstract

Integrated global positioning system (GPS)/strap-down inertial navigation system (SINS) systems are widely used for positioning and attitude determination applications. Kalman filtering is the most common method to combine both sensors. A standard Kalman filter (SKF) relies on the correct definition of the measurement and process models and cannot handle measurements with outliers. The H∞ filter can achieve better robustness performance based on minimising the worst-case estimation error. Different to other adaptive KFs, the H∞ filter controls its robustness by a restriction parameter γ. The γ parameter is usually set and fixed by experience. In this paper, an adaptive strategy to gain a time-varying γ is proposed. The construction of the time-varying parameter is based on a two-segment function of an approximate ratio of the traces of the calculated predicted residual's covariance and the theoretical covariance. The test results indicate that the proposed filtering can achieve the expected robustness performance and efficiency more comprehensively when compared with other filtering methods.