机动目标跟踪的S修正无迹卡尔曼滤波算法

张园; 郭晨; 李树军; 刘淑波; 初俊博

doi:10.16411/j.cnki.issn1006-7736.2015.02.015

大连海事大学学报 >

2015 , Vol. 41 >Issue 2: 84 - 86

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2015.02.015

控制

机动目标跟踪的S修正无迹卡尔曼滤波算法

张园 ,
郭晨 ,
李树军 ,
刘淑波 ,
初俊博

展开

(1.海军大连舰艇学院 a.导弹系;b.海洋测绘系, 辽宁大连 116018; 2.大连海事大学信息科学与技术学院，辽宁大连 116026)

张园（1972-）,女,副教授,博士, E-mail: zhangyuantitao@163.com.

收稿日期: 2014-09-09

修回日期: 2014-09-28

网络出版日期: 2015-06-07

基金资助

国家自然科学基金资助项目（61074053；61374114）；交通部应用基础研究项目（2011-329-225-390）.

收起

The algorithm of S-amended UKF in maneuvering target tracking

ZHANG Yuan ,
GUO Chen ,
LI Shu-jun ,
LIU Shu-bo ,
CHU Jun-bo

Expand

(1a. Department of Missile; 1b. Department of Hydrographic Surveying and Charting, Dalian Naval Academy, Dalian 116018, China; 2. Information Science and Technology College, Dalian Maritime University, Dalian 116026, China)

Received date: 2014-09-09

Revised date: 2014-09-28

Online published: 2015-06-07

Fold

摘要

针对非线性观测条件下的非线性机动目标跟踪问题，借鉴线性滤波中卡尔曼滤波器的S修正防发散思想，对基本无迹卡尔曼滤波算法进行改进，提出S修正无迹卡尔曼滤波（SUKF）方法. 对二维机动目标跟踪的仿真结果表明，该算法与基本UKF算法相比，跟踪精度大幅提高，但计算时间略有增加；与SPPF算法相比，跟踪精度提高，且计算复杂度大幅降低，计算时间大幅缩减.

关键词： 机动目标跟踪; 无迹卡尔曼滤波（UKF）; S修正

本文引用格式

张园 , 郭晨 , 李树军 , 刘淑波 , 初俊博 . 机动目标跟踪的S修正无迹卡尔曼滤波算法[J]. 大连海事大学学报, 2015 , 41(2) : 84 -86 . DOI: 10.16411/j.cnki.issn1006-7736.2015.02.015

Abstract

Aiming at the problem of maneuvering target tracking under nonlinear observation, an S-amended unscented Kalman filtering (SUKF) was developed by using the idea of S-amended anti-divergent method for Kalman filter in linear filtering and improving the performance of unscented Kalman filtering algorithm. Two-dimensional maneuvering target tracking simulations were carried out, and results show that tracking accuracy is significantly improved comparing with the basic UKF algorithm, but calculation time increases slightly. Comparing with the basic UKF algorithm, tracking accuracy is improved, and calculation time reduces signif

Key words： maneuvering target tracking; unscented Kalman filter(UKF); S-amended

参考文献

[1]JULIER S J. The spherical simplex unscented transformation[C]//IEEE. American Control Conference,2003.America:IEEE Press, 2003: 2430-2434.
[2]JULIER S J,UHLMANN J K, “The scaled unscented transformation[C]//IEEE. American Control Conference,2002.America:IEEE Press, 2002: 4555-4559.
[3]JULIER S J, UHLMANN J K. Unscented filtering and nonlinear estimation[J].Proceedings of the IEEE,2004,92(3) :401-422.
[4]WAN E A, VANDER MERWE R. The unscented Kalman filter for nonlinear estimation[C]//IEEE. Adaptive Systems for Signal Processing, Communications and Control symposium 2000. Lake Louise, Alta: IEEE Press,2000:153-158.
[5]朱安福, 景占荣, 高田等. 基于UKF的交互多模型算法[J]. 系统仿真学报, 2009, 21(3):655-657.ZHU An-fu, JING Zhan-rong,TIAN Gao,et al. The interacting multiple model algorithm based on UKF[J]. Journal of system simulation,2009,21(3):655-657.
[6]程水英.无味变换与无味卡尔曼滤波[J].计算机工程与应用,2008,44(24):25-35.CHENG Shui-ying. The unscented transformation and the unscented Kalman filtering. Computer engineer and application,2008,44(24):25-35.
[7]YU Zhi-jun,WEI Jian-ming,LIU Hai-tao. A new adaptive maneuvering target tracking algorithm using articial neural networks[C]//Hong Kong :2008 International Joint Conference on Neural Networks,2008:901-905.
[8]张园.卡尔曼滤波及其军事应用[M].大连:海军大连舰艇学院出版社. 2010: 121-122.
[9]敬忠良. 神经网络跟踪理论及应用[M]. 北京: 国防工业出版社. 1995: 40-41.

Options

文章导航

模态框（Modal）标题

摘要

本文引用格式

Abstract

参考文献