基于卷积神经网络的星载粒子鉴别平台及应用  

Spaceborne particle identification platform and application based on convolutional neural network

作　　者：白超平 张珅毅 张鑫 孙越强 张帅 王子婷 BAI Chaoping;ZHANG Shenyi;ZHANG Xin;SUN Yueqiang;ZHANG Shuai;WANG Ziting(National Space Science Center,the Chinese Acaderny of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院国家空间科学中心,北京100190 [2]中国科学院大学,北京100149

出　　处：《北京航空航天大学学报》2025年第4期1313-1323,共11页Journal of Beijing University of Aeronautics and Astronautics

基　　金：国家自然科学基金(42204180)。

摘　　要：空间辐射粒子的准确鉴别对科研和工程应用至关重要。现有的粒子鉴别方法,包括探测器望远镜法、静电分析飞行时间法、飞行时间能量法及波形分析能量法,已经在实际应用中取得了良好的效果。结合卷积神经网络(CNN)强大的特征提取与分类能力,有望进一步提升粒子能量测量和种类鉴别的精度。基于空间环境探测载荷常用环境,提出一种用于构建在轨CNN粒子鉴别平台的方法,实现粒子种类鉴别。构建了多维度的输入数据集,借助软件平台完成模型的训练与权值的导出,并通过硬件平台完成波形的推断与数据集的扩充。利用建立好的鉴别平台对实际测试得到的中子和伽马波形数据进行训练和测试,并分析软硬件平台鉴别的准确度,完成了平台的验证工作。鉴别平台的建立和应用为未来空间环境探测中粒子测量和鉴别提供了一种新的思路和方法,具有较强的工程实践意义。Accurate identification of space radiation particles is crucial for both scientific research and engineering applications.Existing particle identification methods,including detector telescope methods,electrostatic analysis time-of-flight methods,time-of-flight energy methods,and waveform analysis energy methods,have achieved good results in practical applications.However,by leveraging the powerful feature extraction and classification capabilities of convolutional neural networks(CNN),it is expected to further enhance the precision of particle energy measurement and species identification.Based on common space environment detection payloads,this paper proposes a method to build an on-orbit CNN-based particle identification platform for particle species identification.The platform first constructs a multidimensional input dataset,with model training and weight extraction completed through software platforms,and waveform inference and dataset expansion carried out on the hardware platform.The established identification platform is used to train and test neutron and gamma waveform data obtained from actual tests,and the identification accuracy of both the software and hardware platforms is analyzed,completing the platform verification.The establishment and application of this identification platform provide a new approach and method for future particle measurement and identification in space environment detection,with significant engineering practical implications.

关 键 词：粒子探测 卷积神经网络 星载FPGA平台 脉冲波形鉴别 

分 类 号：V11[航空宇航科学与技术—人机与环境工程]