摘要
无迹卡尔曼滤波是通过确定性采样,以无迹变换为基础,用卡尔曼线性滤波框架而建立起来的,对非线性系统有很好的滤波效果,但当噪声的影响较大时,精确度将会减低。为解决上述问题,提出了一种提高无迹卡尔曼滤波(UKF)精确度的方法,它将观测噪声和系统噪声引入到采样点中,对噪声进行对称采样处理,同时改进了算法过程,增加了无迹卡尔曼滤波的抗干扰性,与常规无迹卡尔曼滤波(UKF)相比,不仅保持了系统的稳定性,而且提高了精确度,最后通过仿真进行了验证。
Unscented Kalman filter（UKF） through uncertainty sampling , basing on the unscented transformation and using the linear Kalman filter framework is established, and they have very good filtering effect and a broad application prospect. However, wben the impact of noise is larger, the precision will be reduced. This article discusses the method improving the accuracy of unscented Kalman filter, the observation noise and the system noise are included in the samples. Through symmetrically sampling the noise and improving the process of the algorithm, the improved UKF increases the anti - jamming of the system, as compared to the conventional unscented Kalman filter. The improved UKF maintains the stability of this system and improves the accuracy, and finally the simulation is made to proved this.
出处
《计算机仿真》
CSCD
北大核心
2010年第3期348-352,共5页
Computer Simulation
基金
国家自然科学基金-中物院NSAF联合基金项目(10776040)
国家自然科学基金项目(60602057)
重庆市科委自然科学基金项目(CSTC
2006BB2373)
重庆市教委自然科学基金项目(KJ060509
KJ080517)
重庆邮电大学自然科学基金项目(A2006-04
A2006-86)
关键词
采样
无迹变换
无迹卡尔曼滤波
Sampling
Unscented transformation （UT）
Unscented Kalman fiher （UKF）
