基于加权自适应平方根容积卡尔曼滤波的GPS/INS组合导航方法

针对GPS/INS组合导航系统中，由于量测噪声统计的不确定性导致平方根容积卡尔曼滤波器(SCKF)滤波精度下降甚至发散的问题，该文提出一种基于加权的自适应SCKF(WASCKF)方法。该方法首先利用移动开窗理论对SCKF新息的协方差阵进行最大似然估计，实现对测量噪声统计特性的在线调整；然后，利用加权理论，依据窗口内不同时刻信息的有用程度的不同而设置相应的权值，增强对窗口内有用信息的利用。最后，将WASCKF方法应用于GPS/INS组合导航系统中进行仿真验证，并与SCKF和ASCKF方法进行比较，结果表明，在测量噪声统计存在不确定情况下，该文所提出方法的速度误差和位置误差的均方根均小于SCKF和ASCKF方法，能够有效地提高GPS/INS组合导航系统对量测噪声统计不确定的自适应能力与导航性能。

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.