摘要
针对冲击脉冲超宽带雷达(Impulse Radio Ultra-Wideband Radar,IR-UWBR)在小样本条件及探测场景复杂等挑战下导致目标识别能力不足的问题,提出基于距离-多普勒图与自适应特征选择网络(Range-Doppler Map and Adaptive Feature Selection Network,RDM-AFSN)的运动目标识别方法。在分析IR-UWBR在慢时间维接收回波信号规律的基础上,建立了IR-UWBR多普勒信息提取模型。同时,深入分析运动目标距离-多普勒图由于背景信息复杂、目标种类多导致图像空间特征差异大的特性,构建基于坐标软阈值去噪模块与空间自适应下采样层的RDM-AFSN目标识别模型。实验结果表明,所提模型能够有效提高小样本条件下对运动目标的分类能力,对不同场景下的同类目标均有较好的识别效果,与常用于地面目标识别的卷积-循环深度网络和图像编码深度网络相比,所提出的RDM-AFSN在识别准确率上分别提高了3.64%和7.53%。
The impulse radio ultra-wideband radar(IR-UWBR)has insufficient target recognition capability under the conditions of small sample size and complex detection scenes.Regarding the abovementioned issue,this paper proposes a moving target recognition method based on range-Doppler map and adaptive feature selection network(RDM-AFSN).An IR-UWBR Doppler information extraction model is established based on the analysis of the law of IR-UWBR receiving the echo signals in the slow time dimension.At the same time,the characteristics of the moving target range-Doppler map,which has large differences in image spatial features due to complex background information and many target types,are deeply analyzed,and a RDM-AFSN target recognition model based on coordinate soft threshold denoising module and spatial adaptive down-sampling layer is constructed.Experimental results demonstrate that the proposed model effectively improves the classification capability of moving targets under small sample sizes and achieves good recognition results for similar targets in different scenarios.Compared to the convolutional-recurrent deep network and image coding deep network commonly used for ground target recognition,the proposed RDM-AFSN improves recognition accuracy by 3.64%and 7.53%,respectively.
作者
黄文宇
熊刚
李龙龙
张淑宁
郁文贤
HUANG Wenyu;XIONG Gang;LI Longlong;ZHANG Shuning;YU Wenxian(School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;School of Electronic and Optical Engineering,Nanjing University of Science and Technology,Nanjing 210094,Jiangsu,China)
出处
《兵工学报》
北大核心
2025年第9期135-145,共11页
Acta Armamentarii
基金
国家自然科学基金项目(62071293)。
