针对强非线性和时变噪声统计特性不明的高动态运动环境下全球卫星导航系统/惯导系统(GNSS/INS)深组合导航系统滤波精确度较差甚至发散的问题, 提出一种自适应混合无迹卡尔曼滤波(UKF)算法。该算法以 UKF算法为基础, 采用混合滤波思想对 UKF滤波算法进行简化; 并根据高动态下系统量测噪声时变, 且易快变、突变的特点, 设计了一种基于渐消记忆指数加权的自适应量测噪声估计器, 实时估计和修正噪声统计量并自适应调节估计周期。仿真结果表明, 在量测噪声变化的情况下, 相比于常规 UKF算法, 本文算法各向定位测速精确度均有所提升, 水平方向精确度提升 60%以上, 效果明显; 此外, 算法耗时减少 18.64%, 说明本文算法能够在提升滤波精确度的同时减少部分计算量。

Aiming at the problem that the filtering accuracy is poor or even divergent in deep integrated Global Navigation Satellite System/Inertial Navigation System(GNSS/INS) in high dynamic motion environment with strong nonlinearity and inaccurate time-varying noise statistics, an adaptive hybrid filtering algorithm is proposed. In the proposed algorithm, the hybrid filtering idea is adopted to simplify the Unscented Kalman Filter (UKF) algorithm. According to the high dynamic system measurement noise time change, especially easy to change quickly and abrupt, an adaptive measurement noise estimator based on fading memory exponent is designed. It estimates and corrects the statistical characteristics in real time, and adaptively regulates the estimation cycle. The simulation results show that, in the case of variation of measurement noise, the accuracy of the algorithm is raised in comparison with the conventional UKF algorithm. The improvement effect of horizontal direction precision is obvious, which is more than 60%. In addition, the time consumption is reduced by 18.64% compared to the conventional UKF algorithm.