电子学报 ›› 2018, Vol. 46 ›› Issue (4): 805-813.DOI: 10.3969/j.issn.0372-2112.2018.04.006

• 学术论文 • 上一篇    下一篇

基于微多普勒效应和AR模型的车辆目标分类方法

李开明1, 张群1,2,3, 罗迎1,2,3, 丁帅帅1, 郭英1   

  1. 1. 空军工程大学信息与导航学院, 陕西西安 710077;
    2. 复旦大学电磁波信息科学教育部重点实验室, 上海 200433;
    3. 信息感知技术协同创新中心, 陕西西安 710077
  • 收稿日期:2016-10-07 修回日期:2017-09-01 出版日期:2018-04-25
    • 作者简介:
    • 李开明 男,1982年12月生,山西应县人.分别于2009年和2016年于空军工程大学获工学硕士学位和工学博士学位.现为空军工程大学信息与导航学院讲师、在站博士后.目前主要从事雷达成像及目标识别领域的研究工作.E-mail:likaiming1982@163.com;张群 男,1964年11月生,陕西合阳人.现为空军工程大学信息与导航学院教授,博士生导师.发表论文400余篇,其中SCI、EI检索200余篇,出版中英文专著各1部.研究方向:雷达信号处理、雷达成像及电子对抗.E-mail:afeuzq@163.com
    • 基金资助:
    • 国家自然科学基金 (No.61471386,No.61571457,No.61701530); 陕西省青年科技新星项目 (No.2016KJXX-49)

A Classification Method of Vehicle Targets Based on Micro-Doppler Effect and Auto Regression Model

LI Kai-ming1, ZHANG Qun1,2,3, LUO Ying1,2,3, DING Shuai-shuai1, GUO Ying1   

  1. 1. School of Information and Navigation, Air Force Engineering University, Xi'an, Shaanxi 710077, China;
    2. Key Laboratory for Information Science of Electromagnetic Waves(Ministry of Education), Fudan University, Shanghai 200433, China;
    3. Collaborative Innovation Center of Information Sensing and Understanding, Xi'an, Shaanxi 710077, China
  • Received:2016-10-07 Revised:2017-09-01 Online:2018-04-25 Published:2018-04-25

摘要: 轮式车辆和履带式车辆的分类是地面目标识别的难点之一.车轮旋转和履带的运动是典型的微动,其产生的微多普勒特征可作为两类车辆目标分类的重要依据.首先,针对短驻留条件下两类车辆目标的雷达回波,分析了两类目标不同微动导致的微多普勒特征差异;其次,基于目标回波短时平稳相关的性质,建立了目标回波的AR模型,采用前后向预测方法得到相应的AR模型系数,提出基于AR模型系数的车辆目标分类方法,并给出AR模型阶数的判定方法,对比了前后向预测系数特征与前向预测系数特征的可分性.最后,结合两类目标的实测数据,在回波预处理的基础上,通过提取实际回波数据的AR模型系数实现了车辆目标的分类,验证了方法的有效性和稳健性.

关键词: 微多普勒, AR模型, 前后向预测, 车辆目标, 分类

Abstract: The classification of the wheeled vehicles and tracked vehicles is one of the difficulties of vehicles recognition.Rotation of the wheels and running of the track are the typical forms of micro-motion,the micro-Doppler signatures of the micro-motion can be the crucial proof for classification of the two kinds of vehicles.Firstly,the micro-Doppler difference of different micro-motion of the two vehicles are analyzed based on the echoes under radar with short dwell time;Secondly,based on the short-time stability and correlation of the echoes,the Auto Regression (AR) model of echoes is established,the forward and backward prediction method is applied in the extraction of AR model coefficients,which are used in the classification of the two kinds of vehicles,and the selection of the order of AR model is operated,the separation abilities between forward and backward prediction coefficients and forward prediction coefficients are compared;At last,after the pre-processing of the measured data,the classification of the two vehicles is accomplished with high accuracy based on the extraction of AR model coefficients.The effectiveness and robustness of the method are proved by the simulation results.

Key words: micro-Doppler, auto regression model, forward and backward prediction, vehicle target, classification

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