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自适应UKF算法在目标跟踪中的应用
引用本文:石勇,韩崇昭. 自适应UKF算法在目标跟踪中的应用[J]. 自动化学报, 2011, 37(6): 755-759. DOI: 10.3724/SP.J.1004.2011.00755
作者姓名:石勇  韩崇昭
作者单位:1.西安交通大学智能网络与网络安全教育部重点实验室、机械制造 系统工程国家重点实验室 电子与信息工程学院综合自动化研究所 西安 710049
基金项目:国家重点基础研究发展计划(973计划)(2007CB311006); 国家自然科学基金(61074176);国家自然科学基金创新研究群体科学基金(60921003)资助~~
摘    要:针对目标跟踪中系统噪声统计特性未知导致滤波发散或者滤波精度不高的问题, 提出了一种自适应无迹卡尔曼滤波(Unscented Kalman filter, UKF)算法.该算法在滤波过程中,利用改进的Sage-Husa估 计器在线估计未知系统噪声的统计特性,并对滤波发散的情况进行判断和抑制, 有效提高了滤波的数值稳定性,减小了状态估计误差. 仿真实验结果表明,与标准UKF算法相比,自适应UKF算法明显改善了目标跟踪的精度和稳定性.

关 键 词:目标跟踪   自适应滤波   无迹卡尔曼滤波
收稿时间:2010-01-27

Adaptive UKF Method with Applications to Target Tracking
SHI Yong HAN Chong-Zhao.Key Laboratory for Intelligent Networks , Network Security of Ministry of Education,State Key Laboratory for Manufacturing Systems Engineering. Adaptive UKF Method with Applications to Target Tracking[J]. Acta Automatica Sinica, 2011, 37(6): 755-759. DOI: 10.3724/SP.J.1004.2011.00755
Authors:SHI Yong HAN Chong-Zhao.Key Laboratory for Intelligent Networks    Network Security of Ministry of Education  State Key Laboratory for Manufacturing Systems Engineering
Affiliation:1.Key Laboratory for Intelligent Networks and Network Security of Ministry of Education, State Key Laboratory for Manufacturing Systems Engineering, Institute of Integrated Automation, School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049
Abstract:To improve low filtering precision and divergence caused by unknown system noise statistics in target tracking, an adaptive UKF(Unscented Kalman filter)is proposed.In the filtering process,by introducing the modified Sage-Husa noise statistic estimator,the new algorithm can estimate the statistical parameters of unknown system noises online and restrain the filtering divergence.Therefore,the filter numerical stability is effectively improved and the state estimation error is reduced. Simulation results show...
Keywords:Target tracking  adative filtering  unscented Kalman filter(UKF)  
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