摘要
视频合成孔径雷达(SAR)具有高帧率成像能力,可作为地面运动目标探测的重要技术手段。经典SAR地面动目标显示(SAR-GMTI)依靠目标回波能量来实现动目标检测,同时动目标阴影亦可作为视频SAR动目标检测的重要途径。然而,由于动目标能量和阴影的畸变或涂抹,依靠单一方式难以实现稳健的动目标检测。该文基于目标能量和阴影的双域联合检测思想,分别通过快速区域卷积神经网络和航迹关联两种技术途径实现了视频SAR动目标联合检测,给出了机载实测数据处理结果,并进行了详细分析。该文方法充分利用目标阴影与能量的特征及空时信息,提升了机动目标检测的稳健性。
Video Synthetic Aperture Radar(SAR)presents great potential in ground moving target detection and tracking through high frame rate and high-resolution imaging.Target Doppler energy is essential for traditional SAR Ground Moving Target Indication(SAR-GMTI),as the target shadow can also be used for detection in video SAR.However,neither of these detection methods can stand alone to achieve robust detection in video SAR,owing to the distortion or smearing of target energy and its shadow.This paper presents the processing results of airborne video SAR real data using the Faster Region-based Convolutional Neural Network(Faster R-CNN)and the traditional track association based on dual-domain joint detection as proposed in the literature.These two approaches successfully utilize the feature and space time information of target Doppler energy and shadow in the detection of a maneuvering target.
作者
丁金闪
仲超
温利武
徐众
DING Jinshan;ZHONG Chao;WEN Liwu;XU Zhong(National Laboratory of Radar Signal Processing,Xidian University,Xi’an 710071,China)
出处
《雷达学报（中英文）》
EI
CSCD
北大核心
2022年第3期313-323,共11页
Journal of Radars
基金
国家自然科学基金(62171358)。
作者简介
通信作者:丁金闪(1980-),男,江苏人,博士,教授,研究方向为视频雷达系统及信号处理技术、新体制雷达等。ding@xidian.edu.cn。
