计算机工程与应用 ›› 2009, Vol. 45 ›› Issue (3): 168-171.DOI: 10.3778/j.issn.1002-8331.2009.03.050

• 图形、图像、模式识别 • 上一篇    下一篇

恒虚警率PDAF的弱点状目标跟踪技术性能分析

艾斯卡尔·艾木都拉,王保柱   

  1. 新疆大学 信息科学与工程学院,乌鲁木齐 830046
  • 收稿日期:2008-07-07 修回日期:2008-09-25 出版日期:2009-01-21 发布日期:2009-01-21
  • 通讯作者: 艾斯卡尔·艾木都拉

PDAF based on CFAR performance comparative research in tracking dim point moving target technology

ASKAR Hamdulla,WANG Bao-zhu   

  1. College of Information Science and Engineering,Xinjiang University,Urumqi 830046,China
  • Received:2008-07-07 Revised:2008-09-25 Online:2009-01-21 Published:2009-01-21
  • Contact: ASKAR Hamdulla

摘要: PDAF与PDAF-AI算法广泛应用于雷达目标检测与微弱点状目标跟踪领域,两者不同之处在于PDAF-AI算法在利用目标位置、运动速率的基础上多加了目标的亮度信息通过Kalman滤波器去估计目标下一时刻的状态。PDAF-AI改变了传统PDAF算法忽略目标亮度信息的不足,它应具有更好的跟踪性能。通过对这两种算法跟踪性能的对比分析研究:带亮度的概率数据关联滤波器技术PDAF-AI总体上比传统的PDAF技术具有更好的实时跟踪性能,然而在强杂波或跟踪区域存在高亮杂波的情况下PDAF-AI的跟踪性能可能会有所下降。

关键词: 恒虚警率, Kalman滤波器, PDAF-AI, 点目标跟踪

Abstract: PDAF and PDAF-AI are widely used in radar targets detection and tracking dim point moving target,the difference is what PDAF-AI algorithm add target’s amplitude information on the basis of target’s position,the moving speed to predict the state of next frame using Kalman filter.This technology has changed the shortcoming of traditional PDAF algorithm which neglects the amplitude information of the target.It should be better tracking performance than PDAF,the paper comparative analysis and research two methods of tracking performance:Probability Data Associating Filiter with the Amplitude Information(PDAF-AI) is better than traditional PDAF algorithm in real-time tracking performance on the whole.However,in certain circumstances the tracking performance of PDAF-AI will declined gradually.

Key words: Constant False Alarm Rate(CFAR), Kalman filter, Probability Data Associating Filiter with the Amplitude Information(PDAF-AI), point target tracking