CAJ | 학술논문

高分辨雷达目标的识别性能取决于目标特征的提取以及分类器的设计.为解决雷达高分辨距离像(HRRP)的方位、平移和幅度敏感性问题,采用了序贯预处理方法,有效提高了HRRP的信噪比.通过提取能较好反映雷达目标散射点回波特性的多维特征向量,设计BP神经网络作为分类器,提出了一种基于目标多维特征向量以及BP神经网络的高分辨雷达目标识别方法.利用在微波暗室测量获得的三种国产飞机模型回波数据进行目标识别处理,实验结果表明,提出的方法能有效地完成三种目标识别任务,在虚警率低于3%的情况下正确识别率优于95%.
고분변뢰체목표적식별성능취결우목표특정적제취이급분류기적설계.위해결뢰체고분변거리상(HRRP)적방위、평이화폭도민감성문제,채용료서관예처리방법,유효제고료HRRP적신조비.통과제취능교호반영뢰체목표산사점회파특성적다유특정향량,설계BP신경망락작위분류기,제출료일충기우목표다유특정향량이급BP신경망락적고분변뢰체목표식별방법.이용재미파암실측량획득적삼충국산비궤모형회파수거진행목표식별처리,실험결과표명,제출적방법능유효지완성삼충목표식별임무,재허경솔저우3%적정황하정학식별솔우우95%.
As for high resolution radar target recognition, the classification performance depends on feature extraction and clas-sifier designing. To solve the problem of sensitivity characteristics of HRRP, sequential preprocessing method is taken, which enhances the signal-to-noise ratio effectively. Some features such as general central moments and distribution entropy of HRRP are extracted to form a multi-dimensional feature vector which can describe the scattering property of target better. A Back-Propagation(BP)neural network classifier is designed. A method for high resolution radar target recognition based on multi-dimensional features and BP neural network is proposed. The measured echoes data samples in the anechoic chamber are processed by means of the BP neural network classifier to discriminate three kinds of target from each other. Experimental results demonstrate that the method can classify targets with performances of over 95%correct classification rate and less than 3%false alarm rate.