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。
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.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第3期168-171,共4页
Computer Engineering and Applications
基金
国家自然科学基金(No.60507005)
新疆维吾尔自治区教育厅高校科研计划科学研究重点资助项目(No.XJEDU2005I04~~
关键词
恒虚警率
KALMAN滤波器
PDAF-AI
点目标跟踪
Constant False Alarm Rate（CFAR）
Kalman filter
Probability Data Associating Filiter with the Amplitude Information（PDAF-AI）
point target tracking
