截断的自适应容积粒子滤波器  

Truncated adaptive cubature particle filter

作　　者：张勇刚[1] 程然 黄玉龙[1] 李宁[1] 

机构地区：[1]哈尔滨工程大学自动化学院,黑龙江哈尔滨150001

出　　处：《系统工程与电子技术》2016年第2期382-391,共10页Systems Engineering and Electronics

基　　金：国家自然科学基金(61371173);中国博士后科学基金(2013M530147;2014T70309);黑龙江省博士后基金(LBH-Z13052;LBHTZ0505);哈尔滨工程大学中央高校基本科研业务费专项基金(HEUCFQ20150407)资助课题

摘　　要：在现有的高斯粒子滤波算法(Gaussian particle filter,GPF)中,粒子的重要性密度函数是由高斯滤波器(Gaussian filter,GF)结合当前最新量测来构建的。在高精度、强非线性的量测条件下,传统GF并不能很好地近似状态真实后验概率密度函数,为了解决这一问题,提出一种截断的自适应容积卡尔曼滤波器,并用其来构建粒子的重要性密度函数,从而推导出了截断的自适应容积粒子滤波器。仿真表明,在高精度、强非线性的量测条件下,所提出的滤波算法比现有的GPF具有更高的估计精度。In the existing Gussian particle filter （GPF）, sample importance density function is constructed through combining the latest measurement based on the Gaussian filter （GF）. However, the true posterior prob- ability density could be approximated badly by the GF under the condition of high accuracy, strong nonlinearity measurements. In order to solve this problem, a truncated adaptive cubature kalman filter is proposed, based on which a new sample importance density function is constructed, so that a truncated adaptive cubature particle filtering method can be derived. Simulation results show that the proposed filtering algorithm has higher estima- tion accuracy than the existing GPF for addressing the nonlinear state estimation with high accuracy and strong nonlinearity measurements.

关 键 词：截断的自适应容积粒子滤波器 后验概率密度函数 高精度 强非线性 

分 类 号：V247.11[航空宇航科学与技术—飞行器设计]