摘要
提出了一种改进的高斯近似(Gaussian approximate,GA)滤波方法,推导了它的一般解和特殊解,并证明了现有的高斯近似滤波方法是所提出的方法的一种特例.在提出的方法中,不需要基于高斯假设重复地产生求积点,而是直接地更新求积点.与现有的高斯近似滤波方法相比,提出的方法利用了量测求积点修正状态求积点,从而可以更好地捕获状态一步预测密度和状态后验密度的非高斯信息和高阶矩信息.此外,提出的方法不仅适用于确定的系统模型而且还适用于随机的系统模型.单变量非平稳增长模型、垂直落体模型、再入飞行器目标跟踪的仿真验证了提出的高斯近似滤波方法的有效性和与现有方法相比的优越性.
In this paper, an improved Gaussian approximate(GA) filtering method is proposed. Its general solution and special solution are derived, and the existing GA filtering method is proved to be its special case of the proposed method.In the proposed method, the quadrature points are no longer generated repeatedly based on Gaussian assumption, but updated directly. As compared with the existing GA filtering method, the proposed method can better capture the non-Gaussian information and high-order moment information of the one-step predicted density and posterior density of state, since the measurement quadrature points in the proposed method are used to correct the state quadrature points.Moreover, the proposed method is suitable for not only deterministic process model but also random process model. The efficiency and superiority of the proposed method are illustrated by simulations of univariate non-stationary growth model,vertically falling body model, and target tracking of re-entry vehicle.
出处
《自动化学报》
EI
CSCD
北大核心
2016年第3期385-401,共17页
Acta Automatica Sinica
基金
国家自然科学基金(61201409
61371173)
中国博士后科学基金(2013M530147
2014T70309)
黑龙江省博士后基金(LBH-Z13052
LBH-TZ0505)
哈尔滨工程大学中央高校基本科研业务费专项基金(HEUCFQ20150407)资助~~
关键词
非线性滤波
高斯近似滤波
高阶矩信息
非高斯信息
贝叶斯估计
Nonlinear filtering
Gaussian approximate(GA) filtering
high-order moment information
non-Gaussian information
Bayesian estimation