基于深度学习的HRRP识别姿态敏感性分析

特征提取是基于高分辨距离像(high resolution range profile, HRRP)的雷达目标识别的关键技术之一。传统人工提取特征的算法, 仅利用浅层结构特征, 无法有效解决姿态敏感性问题, 从而限制了目标识别方法的泛化性。对此, 提出一种基于深度学习的目标识别方法, 并通过详细的姿态角性能测试分析了该方法的应用边界条件。通过构造适合处理HRRP的卷积神经网络(convolutional neural network, CNN)模型, 充分发掘目标深层次姿态不敏感属性特征, 完成高精度目标识别。基于实测数据的实验结果表明, 所提方法具有一定的抗姿态敏感性特性, 边界条件分析可为该方法的工程化应用提供指导。

Pose sensitivity analysis of HRRP recognition based on deep learning

Feature extraction is one of the key technologies for high resolution range profile(HRRP) based radar target recognition. The traditional artificial feature extraction algorithm, which only uses shallow structure features, can not effectively solve the pose sensitivity problem, which limits the generalization of target recognition methods. Thus, a target recognition method based on deep learning is proposed, and the application boundary conditions of this method through detailed pose angle performance test is analyzed. By constructing a convolutional neural network(CNN) model suitable for processing HRRP, the deep-seated pose insensitive attributes of targets are fully explored, and high-precision target recognition is completed. Based on the measured data, the experimental results show that the proposed method has certain anti pose sensitivity characteristics, and the boundary condition analysis can provide guidance for the engineering application of the method.