基于改进概率数据关联滤波的红外小运动目标跟踪

李正周; 金钢; 董能力

doi:10.3724/SP.J.1146.2006.01597

基于改进概率数据关联滤波的红外小运动目标跟踪

doi: 10.3724/SP.J.1146.2006.01597

李正周^{①②;金钢},
金钢,
董能力

基金项目:

国家863计划项目(2002AA731042)，重庆市科委自然科学基金(CS TC2006BB2161)和重庆大学人才引进基金资助课题

计量
- 文章访问数: 3707
- HTML全文浏览量: 76
- PDF下载量: 848
- 被引次数: 0
出版历程
- 收稿日期: 2006-10-19
- 修回日期: 2007-04-09
- 刊出日期: 2008-04-19

A Novel Method for Tracking and Recognizing Infrared Dim and Small Moving Target Based on Modified Probabilistic Data Associating Filter

Li Zheng-zhou^{①②;金钢},
Jin Gang,
Dong Neng-li

摘要

摘要: 在跟踪弱小运动目标过程中，低信噪比和高虚警率使得即使是在波门内也存在较多的虚假目标，加大目标识别的不确定性，降低跟踪的可靠性和精度。该文分析了红外成像系统中目标信号特点，根据概率数据关联滤波的思想，提出了一种基于目标幅度连续性和运动一致性的弱小目标跟踪识别方法，重点比较了极大似然-概率数据关联滤波和改进算法的目标识别与跟踪性能。理论分析和实验结果表明，该方法较大程度上减少了弱小目标跟踪过程中测量点与航迹关联的不确定性，提高了目标识别与跟踪性能。
- 弱小目标识别; 目标跟踪; 概率数据关联滤波; 多特征融合
Abstract: During tracking dim and small moving targets in the electrooptical detecting system, many false alarms extracted in the tracking window for low signal-to-noise ratio and high false alarm ratio debase seriously the performance of target recognition and tracking. To solve this problem, the target characters of the infrared imaging tracking system are analyzed, and based on the idea of the probabilistic data association filter, a modified method, which integrates the continuity and consistency of targets amplitude and motion, is proposed. The emphases are laid on the comparison of the target recognition and tracking performances between the original maximum likehood-probabilistic data associated filter and the proposed method. Some experimental results are included and the performance of target recognition and tracking target is improved greatly.

HTML全文

参考文献(1)

牛秩峰, 沈林成. 基于变形模板的多目标识别与定位[J].电子与信息学报.2006, 28(6):1026-1030浏览[2]程建, 杨杰. 一种基于均值偏移的红外目标跟踪新方法[J]. 红外与毫米波学报, 2005, 24(3): 231-235.Cheng Jian and Yang Jie. Novel infrared object trackingmethod based on mean shift [J]. Journal of InfraredMillimeter Waves, 2005, 24(3): 231-235.[3]张坤华, 王敬儒, 张起衡. 基于边界不变矩的扩展目标识别方法[J]. 光电工程, 2005, 32(8): 1-5.Zhang Kun-hua, Wang Jin-ru, and Zhang Qi-heng. Methodfor recognizing extended target based on boundary invariants[J]. Opto-Electronic Engineering, 2005, 32(8): 1-5.[4]强勇, 焦李成, 保铮. 动态规划算法进行弱目标检测的机理研究[J].电子与信息学报.2003, 25(6):721-727浏览[5]Succcary R and Cohen A, et al.. A dynamic programmingalgorithm for point target detection: practical parameters forDPA [J]. Signal and Data Processing of Small Targets 2001,Proceeding of SPIE Vol.4473: 96-100.[6]Kirubarajan T and Bar-Shalom Y. Low observable targetmotion analysis using amplitude information [J].IEEE Trans.on Aerospace and Electron System.1996, 32(4):1367-1384[7]Chummun M R, Kirubarajan T, and Bar-Shalom Y. Anadaptive early-detection ML/PDA estimator for LO targetswith EO sensors [C]. 2000 IEEE on Aerospace ConferenceProceedings, 18-25 March 2000, Vol.3: 449-464.[8]Kirubarajan T and Bar-Shalom Y. Probabilistic dataassociation techniques for target tracking in clutter [J].Proc.IEEE.2004, 92(3):536-557[9]周宏仁, 敬忠良, 王培德. 机动目标跟踪[M]. 北京: 国防工业出版社, 1991: 221-224, 268-270.Zhou Hong-ren, Jing Zhong-liang, and Wang Pei-de. Trackingof maneuvering targets[M]. Beijing: National DefenceIndustry Press, 1991: 221-224, 268-270.

施引文献

资源附件(0)

访问统计