|Table of Contents|

Adaptive Kalman filter combined with prediction and fault tolerance for target tracking

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2015年01期
Page:
108-114
Research Field:
Publishing date:

Info

Title:
Adaptive Kalman filter combined with prediction and fault tolerance for target tracking
Author(s):
Dai Hongde12Zou Jie2Xu Shenghong1Wang Yongting2 Wu Xiaonan1Wu Guangbin1
1.Department of Control Engineering,Naval Aeronautical and Astronautical University,Yantai 264001,China; 2.Science and Technology on Electron-optic Control Laboratory,Luoyang 471009,China
Keywords:
target tracking Kalman filter adaptive prediction estimation fault tolerant anti-interference on-line matching innovation covariance
PACS:
TP391.41
DOI:
-
Abstract:
An adaptive Kalman filter,based on the online matching of innovation covariance,is presented to overcome the problem of accuracy degrade or even divergence when there exists tremendous modeling errors and to improve the accuracy of target tracking.The area where the target may appear at the next epoch is predicted by one-step Kalman predictor,based the position of the target estimated by Kalman filter at present to avoide searching the whole image to find the target and to reduce the calculation burden.Abnormal measurement detection is also studied and the abnormal measurements are replaced by the Kaman predicted measurement,to avoid the disturbance caused by the abnormal measurement and to increase the anti-interference ability.Experimental results show that the accuracy and robustness of target tracking can be improved by the algorithm presented here.

References:

[1] 胡昭华,宋耀良.一种用于运动跟踪的加窗粒子滤波新算法研究[J].南京理工大学学报,2007,31(3),337-341.
Hu Zhaohua,Song Yaoliang.Motion tracking based on vovel windowing particle filter[J].Journal of Nanjing University of Science and Technology,2007,31(3),337-341.
[2]孔军,汤心溢,蒋敏,等.基于多尺度特征提取的Kalman滤波跟踪[J].红外与毫米波学报,2011,30(5):446-450.
Kong Jun,Tang Xinyi,Jiang Min,et al.Target tracking based on multi-scale feature extraction Kalman filter[J].Journal of Infrared Millim Waves,2011,30(5):446-450.
[3]吴刚,唐振民,程勇,等.灰度共生矩阵纹理特征的运动目标跟踪方法[J].南京理工大学学报,2010,34(4):459-463.
Wu Gang,Tang Zhenmin,Cheng Yong,et al.Object tracking method based on gray level co-occurrence matrix texture charecteristic[J].Journal of Nanjing University of Science and Technology,2010,34(4):459-463.
[4]秦永元,张洪钺,汪叔华.卡尔曼滤波与组合导航原理[M].西安:西北工业大学出版社,2012.
[5]Zarchan P,Musoff H.Fundamentals of Kalman filtering:a practical approach[M].3rd Edition.Virginia:The American Institute of Aeronautics and Astronautics,2009.
[6]Anilkumar A K,Ananthasayanam M R,Philip N K,et al.Adaptive Kalman filter tuning and controlled random search for MMLE with process noise[A].Proceedings of the Proc'AIAA Conference on Flight Mechanics[C].1999,AIAA-1999-4175.
[7]Karasalo M,Hu X.An optimization approach to adaptive Kalman filtering[J].Automatica,2011,47:1785-1793.
[8]Mehra R.On the identification of variances and adaptive Kalman filtering[J].IEEE Transactions on Automatic Control,1970,15:175-184.
[9]Mehra R.Approaches to adaptive filtering[J].IEEE Transactions on Automatic Control,1972,17(5):693-698.
[10]Loebis D,Sutton R,Chudley J,et al.Adaptive tuning of a Kalman ?lter via fuzzy logic for an intelligent AUV navigation system[J].Control Engineering Practice,2004,12:1531-1539.
[11]Jwo D J,Chang F I.A fuzzy adaptive fading Kalman filter for GPS navigation[A].Lecture Notes in Computer Science[C].Berlin:Springer-Verlag,2007:820-831.
[12]戴洪德,陈明,周绍磊,等.基于支持向量机的自适应卡尔曼滤波技术研究[J].控制与决策,2008,23(8):949-952.
Dai Hongde,Chen Ming,Zhou Shaolei,et al.Study of support vector machine based adaptive Kalman filtering[J].Control and Decision,2008,23(8):949-952.
[13]Sorenson H,Sacks J.Recursive fading memory filtering[J].Information Sciences,1971,3:101-119.
[14]Xia Qijun,Rao Ming,Ying Yiqun,et al.Adaptive fading Kalman filter with an application[J].Automatica,1994,30(8):1333-1338.
[15]Ozbek L,Aliev F.Comments on “adaptive fading Kalman filter with application”[J].Automatica,1998,34:1663-1664.
[16]Gao Weixi,Miao Lingjuan,Ni Maolin.Multiple fading factors Kalman filter for SINS static alignment application[J].Chinese Journal of Aeronautics,2011,24(4):476-483.

Memo

Memo:
-
Last Update: 2015-02-28