一种基于强跟踪的改进容积卡尔曼滤波器  

An improved cubature Kalman filters based on strong tracking

作　　者：孙妍[1] 鲁涤强[2] 陈启军[1] 

机构地区：[1]同济大学电子与信息工程学院,上海201804 [2]同济大学磁浮交通工程技术研究中心,上海201804

出　　处：《华中科技大学学报（自然科学版）》2013年第S1期451-454,共4页Journal of Huazhong University of Science and Technology(Natural Science Edition)

基　　金：国家自然科学基金资助项目(91120308);上海市科委基础研究重点资助项目(12JC1408800)

摘　　要：提出了一种将强跟踪滤波算法和容积卡尔曼滤波算法相结合,建立了一种强跟踪CKF算法(STCKF).为了克服模型不准确时容积卡尔曼滤波精度下降问题,通过将强跟踪算法中的渐消因子引入到容积卡尔曼滤波(CKF)的时间更新方程和测量更新方程之中,从而有效地避免模型不准确造成的滤波性能下降.所提出的ST-CKF算法兼具STF鲁棒性强和CKF滤波精度高的优点.通过对一维、多维非线性系统仿真分析,验证了改进容积卡尔曼滤波比容积卡尔曼滤波效果更好.An improved cubature Kalman filter(CKF)combined with strong tracking filter(STF) was proposed,and a strong tracking CKF algorithm(ST-CKF)was established.The fading factor from strong tracking filter was added to time update equations and measurement update equations of the CKF to overcome precision decreasing caused by model uncertainty.The proposed ST-CKF algorithm combined strong robustness of STF with high accuracy of cubature Kalman filter.For the onedimension and multi-dimensional nonlinear system,simulation results show that ST-CKF is better than CKF.

关 键 词：非线性 强跟踪 容积卡尔曼滤波 模型不准确 强跟踪CKF 

分 类 号：TN713[电子电信—电路与系统]