基于迁移学习的雷达杂波幅度统计模型选择  

Radar clutter amplitude statistical model selection based on transfer learning

作　　者：杨立儒 刘永祥 杨威 YANG Liru;LIU Yongxiang;YANG Wei(College of Electronic Science and Technology,National University of Defense Technology,Changsha 410073,China)

机构地区：[1]国防科技大学电子科学学院,湖南长沙410073

出　　处：《系统工程与电子技术》2022年第8期2457-2467,共11页Systems Engineering and Electronics

基　　金：国家自然科学基金(61871384,61921001)资助课题。

摘　　要：在对目标进行探测和识别的过程中,雷达杂波幅度统计模型选择是重要步骤。为了提升杂波幅度统计模型选择的准确率,基于样本分布适配,提出了一种加权再均衡分布适配的迁移学习方法,实现了仿真数据的信息向实测海杂波IPIX数据的迁移。通过与已有算法进行比较,实验结果表明改进后的算法在IPIX数据集上能取得更好的分类准确率,在迁移学习公共数据集Office-Caltech10上的验证结果也表明了算法的普适性。In the process of target detection and recognition, the selection of a radar clutter amplitude statistical model is an important step. In order to improve the accuracy of clutter amplitude statistical model selection, based on the adaptive sample distribution, a weighted rebalance distribution adaptation transfer learning method is proposed to realize the information transfer from simulation data to the measured sea clutter IPIX data. Compared with the existing algorithms, the experimental results show that the improved algorithm can achieve better classification accuracy, and the verification results on the transfer learning public dataset Office-Caltech10 also show the universality of the algorithm.

关 键 词：雷达杂波 IPIX数据 迁移学习 加权再均衡分布适配 

分 类 号：TN957.51[电子电信—信号与信息处理]