INS／GPS紧耦合系统中的模糊自应SRUKF算法

A novel fuzzy adaptive SRUKF algorithm in tightly-coupled INS／GPS systems

摘要:

为解决惯性导航系统(INS)与全球定位系统(GPS)紧耦合中标准无迹卡尔曼滤波(UKF)由于计算舍入误差使协方差矩阵负定和实际应用中由于量测噪声时变而严重影响滤波精度的问题,提出了基于模糊控制理论的自适应平方根无迹卡尔曼滤波(NASRUKF)算法．该算法在滤波过程中不是直接计算协方差矩阵,而是计算协方差矩阵的平方根,从而可以保证协方差矩阵的非负定性;然后根据实时得到的量测信息的实际方差与理论方差的比值,通过设计的模糊控制系统(FCS)实时调整量测噪声矩阵．实验表明:该算法对时变的噪声具有很好的自适应性,相比于UKF算法具有更高的精度并使得系统具有更高的稳定性和鲁棒性．

Abstract:

In tightly-coupled Inertial Navigation System(INS)／Global Position System(GPS) systems, the filtering precision of Kalman filter (UKF) will be affected seriously by the calculation rounding error leading to the covariance matrix negative definiteness and the time-varying measurement noise in practical application. To deal with this problem, an adaptive square root unscented Kalman filter(NASRUKF) based on fuzzy control theory algorithm is proposed. This algorithm not only can ensure the covariance matrix non-negative definiteness, but also can track and adjust the time-varying measurement noise. Experimental results show that the algorithm has a good adaptability for the time-varying noise with higher accuracy,which makes the system have higher stability and robustness in comparison with UKF algorithm.