Multi-Polarization Fusion Few-Shot HRRP Target Recognition Based on Meta-Learning Framework
Qi Liu, Xinyu Zhang, Yongxiang Liu, Kai Huo, Weidong Jiang, Xiang Li
Abstract
Radar high resolution range profiles(HRRP) can represent abundant target structure signatures, which has found wide-spread applications. Recently, radar automatic target recognition(RATR) methods based on deep neural network have achieved promising results because of their strong generalization ability. However, these deep models usually require a large amount of labeled data to optimize the parameters, otherwise they would possibly encounter severe overfitting problem in few-shot condition. In order to solve the above problem, a novel few-shot HRRP target recognition method based on meta-learning framework is proposed, which introduces Long Short-Term Memory(LSTM) based neural network as learner for statistical HRRP data. The proposed method exploits multi-polarization HRRP data for RATR and successfuly improves recognition accuracy and generalization performance in few-shot condition. In the proposed method, a novel learner is designed, which is more suitable for processing one-dimensional statistical HRRP data. We evaluated our method based on an electromagnetic calculation dataset of airplanes and found that the proposed method could successfully fuse multi-polarization HRRP data to provide more effective information for RATR. The experimental results also showed that the proposed method produced improved performance compared with state-of-the-art few-shot learning methods.