摘要
为解决无迹卡尔曼滤波(UKF)算法在组合导航应用中遇到的系统模型不确定、系统噪声统计特性未知以及计算误差较大等问题,提出了模糊自适应强跟踪平方根无迹卡尔曼滤波(FAST-SR-UKF)算法,该算法不仅具有传统UKF的优势,而且包含如下特点:通过模糊自适应强跟踪模块,增强了系统对模型不确定性以及噪声统计参数未知的适应能力;利用平方根滤波的思想,提高了模糊自适应强跟踪无迹卡尔曼滤波算法的数值稳定性,改善了由于计算误差导致的滤波发散问题。仿真结果表明:相对于传统的UKF算法,该算法精度更高、鲁棒性更强。
To address the problems encountered in integrated navigation systems, such as errors introduced by system model uncertainties and unknown noise statistics, a new filtering algorithm which has been named the Fuzzy Adaptive Strong Tracking Square-Root Unscented Kalman Filtering（FAST-SR-UKF）is presented in this paper. The algorithm not only possesses the advantages of the conventional UKF, but also has merits of high filtering accuracy and good stability to system model uncertainties. Experimental results show that the proposed algorithm is superior to the conventional UKF.
出处
《计算机工程与应用》
CSCD
北大核心
2015年第6期254-259,共6页
Computer Engineering and Applications
关键词
组合导航系统
非线性滤波
无迹卡尔曼滤波
模糊逻辑
integrated navigation systems
nonlinear filtering
unscented Kalman filters
fuzzy logical
