基于深度学习的HRRP识别姿态敏感性分析

doi:10.12305/j.issn.1001-506X.2022.03.12

系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (3): 802-807.doi: 10.12305/j.issn.1001-506X.2022.03.12

基于深度学习的HRRP识别姿态敏感性分析

孙晶明^1,^2,*, 虞盛康^1,², 孙俊^1,²

1. 南京电子技术研究所, 江苏南京 210039
2. 中国电子科技集团公司智能感知技术重点实验室, 江苏南京 210039

收稿日期:2021-01-13 出版日期:2022-03-01 发布日期:2022-03-10
通讯作者: 孙晶明
作者简介:孙晶明(1984—), 男, 高级工程师, 博士, 主要研究方向为雷达目标识别、深度学习|虞盛康(1989—), 男, 高级工程师, 博士, 主要研究方向为雷达目标检测与识别、机器学习|孙俊(1974—), 男, 研究员级高级工程师, 博士, 主要研究方向为新体制雷达、雷达信号处理
基金资助:
国家自然科学基金(U19B2031)

Pose sensitivity analysis of HRRP recognition based on deep learning

Jingming SUN^1,^2,*, Shengkang YU^1,², Jun SUN^1,²

1. Nanjing Research Institute of Electronics Technology, Nanjing 210039, China
2. Key Laboratory of Intellisense Technology, China Electronics Technology Group Corporation, Nanjing 210039, China

Received:2021-01-13 Online:2022-03-01 Published:2022-03-10
Contact: Jingming SUN

摘要/Abstract

摘要：

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。

关键词: 雷达目标识别, 高分辨距离像, 姿态敏感性, 深度学习, 卷积神经网络

Abstract:

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.

Key words: radar target recognition, high resolution range profile (HRRP), pose sensitivity, deep learning, convolutional neural network (CNN)

中图分类号:

TN957.52

孙晶明, 虞盛康, 孙俊. 基于深度学习的HRRP识别姿态敏感性分析[J]. 系统工程与电子技术, 2022, 44(3): 802-807.

Jingming SUN, Shengkang YU, Jun SUN. Pose sensitivity analysis of HRRP recognition based on deep learning[J]. Systems Engineering and Electronics, 2022, 44(3): 802-807.

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实验编号	训练集样本姿态角间隔/(°)	训练集样本量	测试集样本量
1	10	21 600	86 400
2	20	10 800	86 400
3	30	7 200	86 400
4	40	6 000	86 400
5	50	4 800	86 400
6	60	3 600	86 400

实验编号	训练集样本姿态角域/(°)	训练集样本量	测试集样本量
1	0~20	12 000	96 000
2	80~100	12 000	96 000
3	160~180	12 000	96 000

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基于深度学习的HRRP识别姿态敏感性分析

Pose sensitivity analysis of HRRP recognition based on deep learning

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