舰船电子工程2012，Vol.32Issue(1)：31-32,50,3.
基于采样的非线性滤波算法比较
Comparison of Nonlinear Filtering Algorithms Based on Sampling
摘要
Abstract
In dealing with real-time estimation of dynamic system,such as target tracking.The extended Kalman filter（EKF） is used as a state estimation method to improve the estimation accuracy.However,there is estimation error in linearizing system due to the defects of EKF in nonlinear estimation,which affects the accuracy of target tracking.Three new nonlinear filter algorithms are presented in order to yield higher estimation accuracy.The three methods are unscented Kalman filter（UKF） and particle filter（PF） and UPF.the algorithms are analyzed.The applications of the algorithms to the state estimation models are compared.Finally,the algorithms are compared through a tracking model simulation.Experiment results show that the proposed algorithms outperforms EKF at convergence speed,consistency and tracking precision.关键词
无味卡尔曼滤波/简单粒子滤波/无味粒子滤波/非线性/目标跟踪Key words
unscented Kalman filter/particle filter/unscented particle filter/nonlinear/target tracking分类
信息技术与安全科学引用本文复制引用
赵侃,漆德宁..基于采样的非线性滤波算法比较[J].舰船电子工程,2012,32(1):31-32,50,3.基金项目
安徽省自然科学基金 （）
中国博士后科学基金（编号：200801493,20080430223）资助 （）
