一种新的混合迭代UKF  

A New Kind of Hybrid Iterated Unscented Kalman Filter

作　　者：常国宾[1] 许江宁[1] 胡柏青[1] 纪兵[1] 

机构地区：[1]海军工程大学电气与信息工程学院,武汉市解放大道717号430033

出　　处：《武汉大学学报（信息科学版）》2012年第6期701-703,共3页Geomatics and Information Science of Wuhan University

基　　金：国家自然科学基金资助项目(40904018);东南大学微电子机械系统教育部重点实验室开放基金资助项目(201001)

摘　　要：从统计线性回归的角度对无味变换(unscented transformation,UT)进行分析,推导了迭代无味卡尔曼滤波(iterated unscented Kalman filter,IUKF)。针对IUKF计算量大的问题,结合弦线迭代法和IUKF,得到了一种新的混合迭代无味卡尔曼滤波器。数值仿真的结果表明,新滤波算法的精度优于扩展卡尔曼滤波、迭代扩展卡尔曼滤波和无味卡尔曼滤波,并可以有效降低IUKF的计算量。Unscented transformation is analyzed through the viewpoint of statistical linear re- gression, and iterated unscented Kalman filter （IUKF） is derived. The so-called hybrid iter- ated unscented Kalman filter is presented by incorperating secant method into IUKF in order to cope with the high-computation-cost problem. New filtering method is introduced into the example of univariate nonstationary growth model. Simulation results show that new method outperforms extended Kalman filter, iterated extended Kalman filter and unscented Kalman filter. The computation cost can be reduced relative to IUKF.

关 键 词：非线性滤波 迭代无味卡尔曼滤波 弦线法 单变量非平稳增长模型 

分 类 号：P228.41[天文地球—大地测量学与测量工程]