雷达学报2026，Vol.15Issue(2)：583-604,22.DOI:10.12000/JR25123
仿真数据辅助的雷达HRRP小样本目标识别方法
Few-shot Radar High-resolution Range Profile:Target Recognition with Simulated Data Assistance
摘要
Abstract
Research on target recognition using radar High-Resolution Range Profiles(HRRPs)is extensive and diverse in methodology.In particular,the application and development of deep learning to radar HRRP target recognition have enabled efficient,precise target perception directly from radar echoes.However,deep learning-based recognition networks rely on large amounts of training data.For non-cooperative targets,due to limited radar system parameters and rapid target attitude variations,acquiring adequate HRRP training samples that comprehensively cover target attitudes in advance is difficult in practice.Consequently,deep recognition networks are prone to overfitting and exhibit considerably degraded generalization capability.To address these issues,and given the ease of obtaining full-attitude electromagnetic simulation data for the target,this paper leverages simulated data as auxiliary information to mitigate the small-sample-size problem through data augmentation and cross-domain knowledge-transfer learning.For data augmentation,based on the analysis of differences in mean and variance between simulated and measured HRRPs within a given attitude-angle range,a linear transformation is applied to a set of simulated HRRPs spanning the same angular domain as a small set of measured HRRPs.This adjustment ensures that the simulated data's mean and variance match the characteristics of the measured HRRPs,thereby achieving data augmentation that approximates the true distributional properties of HRRPs.Meanwhile,for cross-domain knowledge transfer learning,the proposed method introduces a domain alignment strategy based on generative adversarial constraints and a class alignment strategy based on contrastive learning constraints.These approaches draw the domain features of full-attitude simulation—strong discriminability and generalizability—closer to the measured domain features on a class-by-class basis,thereby further aiding learning from the measured domain data and leading to substantial improvements in few-shot recognition performance.Experimental results based on electromagnetic simulated and measured HRRP data for three and ten types of aircraft and ground vehicle targets,respectively,demonstrate that the proposed method yields superior recognition robustness compared with existing few-shot recognition methods.关键词
雷达目标识别/高分辨距离像/小样本/仿真数据辅助/数据扩充/跨域知识迁移学习Key words
Radar target recognition/High-Resolution Range Profile(HRRP)/Few-shot learning/Simulated data assistance/Data expansion/Cross-domain knowledge transfer learning分类
信息技术与安全科学引用本文复制引用
陈健,於刚,杜兰,董文强,郭昱辰..仿真数据辅助的雷达HRRP小样本目标识别方法[J].雷达学报,2026,15(2):583-604,22.基金项目
教育部联合基金(8091B03032401),国家自然科学基金(U24B20137,U21B2039),航空科学基金(20230020081006)Joint Fund of the Ministry of Education of China(8091B03032401),The National Natural Science Foundation of China(U24B20137,U21B2039),The Aviation Science Foundation(20230020081006) （8091B03032401）
