西安电子科技大学学报（自然科学版）2016，Vol.43Issue(2)：23-28,6.DOI:10.3969/j.issn.1001-2400.2016.02.005
采用多任务稀疏学习的雷达HRRP小样本目标识别
Radar HRRP target recognition by utilizing multitask sparse learning with a small training data size
摘要
Abstract
A statistical modeling method based on multitask sparse learning is proposed to realize the recognition of the high resolution range profile (HRRP) with a small training data size. The statistical modeling of each training aspect-frame is considered as a single task in our method. Since the training aspect-frames are not independent but inter-related, they can share a compact dictionary to make full use of the information. However, with the different targets and the aspect sensitivity of the same target, it is usually hard to assess the task relatedness, and joint learning with unrelated tasks may degrade the recognition performance. Therefore, we adopt the Bernoulli-Beta prior to learn the needed atoms of each aspect-frame automatically with the given training data. Then the relatedness between frames is determined by the number of shared atoms, and multitask learning can be realized adaptively. The recognition experiments of the measured HRRP data demonstrate the performance of the proposed method.关键词
雷达目标识别/高分辨距离像/稀疏贝叶斯/多任务学习Key words
radar target recognition/HRRP/sparse Bayesian/multitask learning分类
信息技术与安全科学引用本文复制引用
徐丹蕾,杜兰,王鹏辉,刘宏伟..采用多任务稀疏学习的雷达HRRP小样本目标识别[J].西安电子科技大学学报（自然科学版）,2016,43(2):23-28,6.基金项目
国家自然科学基金资助项目(61271024 ,61201296 ,61322103) （61271024 ,61201296 ,61322103）
全国优秀博士学位论文作者专项资金资助项目(FANEDD-201156) （FANEDD-201156）
中央高校基本科研业务费专项资金资助项目(K5051302010) （K5051302010）
