基于序贯UKF的GNSS/CNS/SINS组合导航最优融合算法

组合导航系统在动态环境下具有强非线性，为提高GNSS/CNS/SINS组合导航系统的导航精度，提出一种基于序贯UKF的多传感器最优融合算法。首先，建立GNSS/CNS/SINS组合导航系统的非线性状态方程及2个子滤波器的线性量测方程；然后，对标准UKF的量测更新过程进行简化，简化UKF算法具有与标准UKF算法相同的滤波精度，且具有计算量低的特性；最后，将序贯滤波算法与简化UKF算法结合，提出多传感器组合导航系统的序贯UKF最优融合算法。仿真结果表明，本文序贯UKF算法不仅可提高系统解算的实时性，并且相对于集中线性卡尔曼滤波算法及经典集中线性UKF算法具有较高的滤波精度。

An Optimal Fusion Algorithm for GNSS/CNS/SINS Integrated Navigation Based on Sequential UKF

Integrated navigation system has strong nonlinear characteristic in dynamic environment, so we propose an optimal multi-sensor fusion algorithm based on sequential UKF to improve the navigation accuracy of GNSS/CNS/SINS integrated navigation system. Firstly, we establish the nonlinear state equation and the linear measurement equations of two subfilters for GNSS/CNS/SINS integrated navigation system. Then, by simplifying the measurement updating process of the standard UKF algorithm, we design a simplified UKF algorithm that has the same filtering accuracy as the standard UKF algorithm and has characteristics of low computation. Finally, we propose the sequential UKF optimal fusion algorithm for multi-sensor integrated navigation system, by combining the sequential filtering algorithm with the simplified UKF algorithm. The simulation results show that the sequential UKF algorithm not only improves the real-time performance of the system, but also has higher filtering accuracy than the conventional centralized Kalman filter algorithm and the classical centralized linear UKF algorithm.