基于微多普勒效应和多级小波分解的轮式履带式车辆分类研究  

Study on Classification of Wheeled and Tracked Vehicles Based on Micro-Doppler Effect and Multilevel Wavelet Decomposition

作　　者：李彦兵[1] 杜兰[1] 刘宏伟[1] 王宝帅[1] 

机构地区：[1]f西安电子科技大学雷达信号处理国家重点实验室,西安710071

出　　处：《电子与信息学报》2013年第4期894-900,共7页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(60901067,61001212);新世纪优秀人才支持计划(NCET-09-0630);长江学者和创新团队发展计划(IRT0954);中央高校基本科研业务费专项资金联合资助课题

摘　　要：短驻留时间条件下的轮式和履带式车辆目标分类对于战场侦察雷达系统目标识别功能的引入具有应用价值。该文基于微多普勒效应对轮式和履带式车辆的雷达回波进行了分析,针对这两种车辆的雷达回波中包含的微多普勒信号的差异,提出一种基于多级小波分解的分类方法。该方法首先使用多抽样率信号处理减轻了目标平动速度变化对分类结果的影响,其次通过对目标的平动和微动分量进行分离,提取了较好描述类间目标差异性的特征。基于实测数据的实验结果表明该方法具有较好的分类性能,同时对目标速度的变化具有稳健性。Classification of moving vehicles within short dwell time is a promising way to the introduction of the target identification function to battlefield surveillance radar system. In this paper, radar returned echoes of moving wheeled and tracked vehicle are analyzed using micro-Doppler effect. According to the distinction between the micro-Doppler signals of these two kinds of vehicles, a wavelet transform based classification method is proposed. In this method, the influence induced by the change of main bulk velocity is alleviated by using multirate signal processing and the distinctions between wheeled and tracked vehicles are well depicted due to the separation of the bulk motion and micro motion components. Experiment results based on the measured data show the proposed method simultaneously achieves good classification performance and robustness to the change of the bulk velocity.

关 键 词：雷达目标分类 微多普勒 小波分解 

分 类 号：TN959.17[电子电信—信号与信息处理]