简化的混合估计算法及其在GPS／SINS深组合中的应用

为解决GPS/SINS深组合导航系统滤波的非线性和噪声的不确定性的问题,针对深组合模型特点,设计了一种简化的基于U滤波的多模型混合估计滤波器。根据系统模型中状态方程是线性方程、观测方程是非线性方程的特点,提出了一种简化的U滤波算法(Ultra tight coupling unscented Kalman filter,UTCUKF),然后针对噪声变化建立了非线性模型,多模型混合估计滤波器的输出为各滤波器的概率加权融合,因此模型概率是根据噪声变化而调整的,从而也使系统输出对噪声变化具有一定自适应能力。最后进行了仿真,并与基于普通U滤波的多模型混合估计算法进行了比较。结果表明,本文算法的解算时间短,模型切换速度更快,而估计的精确度与同条件下的基于普通U滤波的多模型混合估计算法相当,更符合深组合系统高动态的要求。

Hybrid Estimation Algorithm Based on Simplified UKF for Ultra Tight Coupling GPS/SINS System

According to the feature of ultra tight coupling GPS/SINS system,in which the state equation is linear and the measurement equation is nonlinear,a new simplified unscented Kalman filter(UKF) is proposed.Then a new multiple model hybrid estimation algorithm based on the simplified UKF is presented to solve the problem of nonlinear filtering and noise modeling.The uncertainty of the noise can be described by a set of switching models.The output of the multiple model hybrid estimation filter is the weighted sum of a bank of parallel filters.The self-adaptive filtering for different noises can be performed by the adjustment of all models′ weights.Finally,the simulation and comparison are given.The application of the algorithm on ultra tight coupling GPS/SINS system shows a higher switching speed of the algorithm than that of hybrid estimation based on common UKF,and indicates that the algorithm has the same accuracy with the common one in the same condition.The algorithm meets the demands of ultra tight coupling GPS/SINS system.