现代雷达2025，Vol.47Issue(5)：16-20,5.DOI:10.16592/j.cnki.1004-7859.20250102001
面向雷达目标识别的一种在线迁移学习框架
An Online Transfer Learning Framework for Radar Target Recognition
摘要
Abstract
The contradiction between the requirements for reliable,efficient,and precise target recognition performance and the challenges in constructing comprehensive target databases demands that radar target recognition systems possess dynamic learning capabilities.These capabilities enable dynamic updates of data and models,as well as continuous improvement in recognition per-formance.The realization of functions such as sample self-labeling and model self-updating serves as prerequisite for achieving this objective.To address the practical need for performance self-enhancement in radar target recognition applications,an online trans-fer learning framework by integrating concepts from online learning and transfer learning is proposed in this study.Featuring a closed-loop structure,the framework combines online learning with transfer learning technologies to achieve self-iterative model op-timization through sample annotation and model fine-tuning,thereby automatically completing tasks such as sample labeling and model updating.Experimental results based on simulated data demonstrate that the proposed framework significantly enhances radar target recognition accuracy.With advantages including streamlined processes and rapid deployment,the framework exhibits strong engineering practicality.关键词
雷达目标识别/样本自标注/模型自更新/在线学习/迁移学习Key words
radar target recognition/sample self-labeling/model self-updating/online learning/transfer learning分类
电子信息工程引用本文复制引用
杨予昊,孙晶明,张强,晏媛,王众..面向雷达目标识别的一种在线迁移学习框架[J].现代雷达,2025,47(5):16-20,5.基金项目
国家自然科学基金资助项目(U22B2059) （U22B2059）
