Lu Wang, Zhang Yasheng, Xu Can, Lin Caiyong, Huo Yurong
Graduate School, Space Engineering University, Beijing 101416, China.
Space Engineering University, Beijing 101416, China.
Sensors (Basel). 2019 Apr 29;19(9):2008. doi: 10.3390/s19092008.
A novel satellite target recognition method based on radar data partition and deep learning techniques is proposed in this paper. For the radar satellite recognition task, orbital altitude is introduced as a distinct and accessible feature to divide radar data. On this basis, we design a new distance metric for HRRPs called normalized angular distance divided by correlation coefficient (NADDCC), and a hierarchical clustering method based on this distance metric is applied to segment the radar observation angular domain. Using the above technology, the radar data partition is completed and multiple HRRP data clusters are obtained. To further mine the essential features in HRRPs, a GRU-SVM model is designed and firstly applied for radar HRRP target recognition. It consists of a multi-layer GRU neural network as a deep feature extractor and linear SVM as a classifier. By training, GRU neural network successfully extracts effective and highly distinguishable features of HRRPs, and feature visualization technology shows its advantages. Furthermore, the performance testing and comparison experiments also demonstrate that GRU neural network possesses better comprehensive performance for HRRP target recognition than LSTM neural network and conventional RNN, and the recognition performance of our method is almost better than that of other several common feature extraction methods or no data partition.
本文提出了一种基于雷达数据划分和深度学习技术的新型卫星目标识别方法。针对雷达卫星识别任务，引入轨道高度作为一个独特且可获取的特征来划分雷达数据。在此基础上，我们为高分辨距离像（HRRPs）设计了一种新的距离度量，称为归一化角距离除以相关系数（NADDCC），并应用基于此距离度量的层次聚类方法对雷达观测角域进行分割。利用上述技术，完成了雷达数据划分并获得了多个高分辨距离像数据簇。为了进一步挖掘高分辨距离像中的本质特征，设计了一种门控循环单元-支持向量机（GRU-SVM）模型，并首次将其应用于雷达高分辨距离像目标识别。它由作为深度特征提取器的多层GRU神经网络和作为分类器的线性支持向量机构成。通过训练，GRU神经网络成功提取了高分辨距离像有效且具有高度区分性的特征，并且特征可视化技术显示了其优势。此外，性能测试和对比实验还表明，与长短期记忆（LSTM）神经网络和传统循环神经网络（RNN）相比,GRU神经网络在高分辨距离像目标识别方面具有更好的综合性能，并且我们方法的识别性能几乎优于其他几种常用的特征提取方法或未进行数据划分的情况。
