A GPS/INS Integrated Navigation Method Based on Weighting Adaptive Square-root Cubature Kalman Filter

In the GPS/INS integrated navigation system, the filtering precision of Square-root Cubature Kalman Filter (SCKF) will decrease or even diverge resulting from the uncertainty of the measured noise statistics, therefore, a Weighting Aaptive Square-root Cubature Kalman Filter (WASCKF) method is proposed in this paper. Firstly, the moving window method is employed to conduct the maximum likelihood estimation of the covariance matrix of SCKF, in order to realize the on-line adjustment of the statistical characteristics of the measured noise. Then, the weighting theory is utilized to set the corresponding weights according to the usefulness of the information at different times in the window, thus it takes great use of effective information in the window. Finally, the WASCKF is applied to the GPS/INS integrated navigation system for simulation and verification, and comparing with the SCKF and ASCKF methods. The results indicate that the mean square root of velocity errors and position errors of the proposed method are less than SCKF and ASCKF, and it can effectively improve the adaptive capability and navigation performance of GPS/INS integrated navigation system with the measured noise uncertainty.