HRRP稀疏自编码器深层特征与散射中心特征的关联性研究

doi:10.12305/j.issn.1001-506X.2021.11.02

Abstract

Abstract:

A sparse auto-encoder network is used to learn and train one-dimensional high resolution range profile (HRRP) of typical targets. A comprehensive weight coefficient is defined based on the weight coefficient matrix of each layer. By comparing the weight coefficient and dimension reduction feature with the scattering center feature, it is found that there is a certain correlation between the deep feature of sparse auto-encoder and the scattering center feature. And the physical meaning of the comprehensive weight coefficient and deep dimension reduction feature is explained in this paper. Firstly, a sparse auto-encoder network is constructed for HRRP. After deep learning, the weight coefficient after training and the feature after dimension reduction are obtained, and the correlation with the position feature and intensity distribution feature of the scattering center is studied. The results show that the comprehensive weight coefficient matrix is a dictionary-like coefficient matrix closely related to the scattering center, which reflects the possible molecule set of the strong scattering center position changing with the angle in the range domain; and the dimension reduction feature can realize the learning and extraction of the strong scattering center, which reflects the change of the strong scattering center position and intensity with the angle. Finally, the influence of the number of training layers and the dimension reduction dimension on the learning and training results is analyzed, which can guide the selection of the subsequent network parameters. For the first time, this paper studies the interpretability of deep learning features for radar HRRP data, which provides a useful guidance for the subsequent extensive application of deep learning in radar data processing.

Key words: high resolution range profile (HRRP), sparse auto-encoder, deep feature, scattering center feature

CLC Number:

TN95

Chaoying HUO, Hua YAN, Xuejian FENG, Hongcheng YIN, Xiaoyu XING, Jinwen LU. Correlation research between deep features of HRRP sparse auto-encoder and scattering center features[J]. Systems Engineering and Electronics, 2021, 43(11): 3040-3053.

Figures/Tables 22

Fig.1

Fig.2

Fig.3

Table 1

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

Fig.14

Fig.15

Fig.16

Fig.17

Table 2

Fig.18

Fig.19

Fig.20

References 21

1	袁莉. 基于高分辨距离像的雷达目标识别方法研究[D]. 西安: 西安电子科技大学, 2007.
	YUAN L. Study of radar target recognition method base on high range resolution profile[D]. Xi'an: Xidian University, 2007.
2	高倩, 刘家学, 吴仁彪. 基于平均模板的HRRP自动目标识别[J]. 现代雷达, 2003, 25 (3): 14- 17. doi: 10.3969/j.issn.1004-7859.2003.03.005
	GAO Q , LIU J X , WU R B . Automatic target recognition based on HRRP mean-templates[J]. Modern Radar, 2003, 25 (3): 14- 17. doi: 10.3969/j.issn.1004-7859.2003.03.005
3	高倩, 吴仁彪. 一种基于SVM分类器的HRRP-ATR方法[J]. 现代雷达, 2004, 26 (5): 20- 23. doi: 10.3969/j.issn.1004-7859.2004.05.006
	GAO Q , LIU J X . HRRP-ATR based on SVM classifier[J]. Modern Radar, 2004, 26 (5): 20- 23. doi: 10.3969/j.issn.1004-7859.2004.05.006
4	余责永, 刘汪渡, 谢永亮. 基于SVM的雷达目标一维距离像识别[J]. 舰船电子工程, 2009, 29 (9): 102- 104.
	YU Z Y , LIU W D , XIE Y L . Radar target recognition using range profiles based on SVM[J]. Ship Electronic Engineering, 2009, 29 (9): 102- 104.
5	DU L , LIU H W , BAO Z , et al. Radar automatic target reco-gnition using complex high-resolution range profiles[J]. IET Radar Sonar Navigation, 2007, 1 (1): 18- 26. doi: 10.1049/iet-rsn:20050119
6	张小英, 王宝发, 刘铁军. 基于PCA-LVQ的雷达目标一维距离像识别[J]. 系统工程与电子技术, 2005, 27 (8): 1373- 1375. doi: 10.3321/j.issn:1001-506X.2005.08.013
	ZHANG X Y , WANG B F , LIU T J . 1-D range profile recognition of radar targets using PCA-LVQ[J]. Systems Engineering and Electronics, 2005, 27 (8): 1373- 1375. doi: 10.3321/j.issn:1001-506X.2005.08.013
7	李波, 任红梅, 肖志河. 人工智能在雷达应用中的限制和发展前景[J]. 军事文摘, 2019, 435 (3): 42- 44.
	LI B , REN H M , XIAO Z H . Limitation and development prospect of artificial intelligence in radar application[J]. Military Digest, 2019, 435 (3): 42- 44.
8	徐高贵, 殷红成, 袁莉, 等. 一种基于卷积神经网络的HRRP序列空间目标识别方法[J]. 中国传媒大学学报(自然与科学版), 2019, 26 (3): 40- 44.
	XU G G , YIN H C , YUAN L , et al. Spatial target recognition method of HRRP sequence based on convolutional neural network[J]. Journal of Communication University of China (Science and Technology), 2019, 26 (3): 40- 44.
9	王容川, 庄志洪, 王宏波, 等. 基于卷积神经网络的雷达目标HRRP分类识别方法[J]. 现代雷达, 2019, 41 (5): 33- 38.
	WANG R C , ZHUANG Z H , WANG H B , et al. HRRP classification and recognition method of radar target based on convolutional neural network[J]. Modern Radar, 2019, 41 (5): 33- 38.
10	陆金文, 殷红成, 盛晶, 等. 基于改进的一维卷积神经网络的高分辨距离像识别方法[J]. 电光与控制, 2020, 27 (8): 19- 22.
	LU J W , YIN H C , SHENG J , et al. High-resolution range profile recognition based on improved one-dimensional convolutional neural network[J]. Electronics Optics & Control, 2020, 27 (8): 19- 22.
11	JARMO L, VISA K. Deep learning for HRRP-based target recognition in multistatic radar systems[C]//Proc. of the IEEE Radar Conference, 2016.
12	冯博. 雷达高分辨距离像特征提取与识别方法研究[D]. 西安: 西安电子科技大学, 2015.
	FENG B. Feature extraction and recognition methods for radar high range resolution profiles[D]. Xi'an: Xidian University, 2015.
13	冯博, 陈渤, 王鹏辉, 等. 基于稳健深层网络的雷达高分辨距离像目标特征提取算法[J]. 电子与信息学报, 2014, 36 (12): 2949- 2955.
	FENG B , CHEN B , WANG P H , et al. Feature extraction method for radar high resolution range profile targets based on robust deep networks[J]. Journal of Electronics & Information Technology, 2014, 36 (12): 2949- 2955.
14	翟颖, 陈渤. 基于稳健变分自编码器模型的雷达高分辨距离像目标识别算法[J]. 电子学报, 2020, 48 (6): 1149- 1150.
	ZHAI Y , CHEN B . Robust variational auto-encoder for radar HRRP target recognition[J]. Acta Electronica Sinica, 2020, 48 (6): 1149- 1150.
15	张增, 任汉付. 基于SAE-HMM模型的雷达目标高分辨率距离像识别方法[P]. 中国: CN 109871907 A, 20190611.
	ZHANG Z, REN H F. Radar target high resolution range profile recognition method based on SAE-HMM model[P]. China: CN 109871907 A, 20190611.
16	CHENG X Y , PENG X Y , LI J B , HUO C Y . Sparse autoencoder based manifold analyzer model of multi-angle target feature[J]. IEEE Access, 2020, 8, 153250- 153263.
17	BYRD R H , NOCEDAL J , SCHNABEL R B . Representations of quasi-Newton matrices and their use in limited memory me-thods[J]. Mathematical Programming, 1994, 63 (1/3): 129- 156.
18	肖慧. 物理光学电磁建模方法的研究与实现[D]. 长沙: 国防科学技术大学, 2003.
	XIAO H. Research on and implementation of electromagnetic modeling method based on physical optics[D]. Changsha: National University of Defense Technology, 2003.
19	崔索民, 吴振森, 方大纲. 一种计算多面体目标RCS的等效边缘电磁流公式[J]. 微波学报, 1997, 13 (1): 20- 25.
	CUI S M , WU Z S , FANG D G . A novel formula of equivalent edge current for calculating RCS of polyhedral structure[J]. Journal of Microwaves, 1997, 13 (1): 20- 25.
20	殷红成, 朱国庆, 董纯柱, 等. 基于自适应射线管分裂的多次反射计算方法[J]. 系统工程与电子技术, 2013, 35 (4): 700- 706.
	YIN H C , ZHU G Q , DONG C Z , et al. Efficient multi-reflection computational method based on adaptive ray tube splitting[J]. Systems Engineering and Electronics, 2013, (4): 700- 706.
21	QUINQUIS A, DEMETER S, RADOIE. Enhancing the resolution of radar range profiles using a class of subspace eigenanalysis based techniques[C]//Proc. of the Digital Signal Processing, 2001: 288-303.

标准体名称	标准体尺寸	坐标中心	建模方法
三面角	直角边长 600 mm、壁厚5 mm	直角中心位于(5,0,0)	PO+EEC+ SBR(3次反射)
二面角	内板尺寸 1 m×0.5 m、壁厚5 mm	直角中心位于(0,0,0)	PO+EEC+ SBR(2次反射)
立方体	边长1 m	直角中心位于(0,0,0)	PO+EEC

降维维数	冗余度/%
256	80.4
64	65.6
16	56.2
15	53.3
14	50.0
13	46.1
12	41.6
11	45.4
10	40.0
9	33.3
8	37.5
7	28.6
6	33.3
5	40.0

[1]	Jingming SUN, Shengkang YU, Jun SUN. Radar small sample target recognition method based on meta learning and its improvement [J]. Systems Engineering and Electronics, 2022, 44(6): 1839-1845.
[2]	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.
[3]	Bo DAN, Zhequan FU, Shan GAO, Tao JIAN. Full-polarization high resolution range profile recognition technology for sea surface target based on convolutional neural network [J]. Systems Engineering and Electronics, 2022, 44(1): 108-116.
[4]	Liang ZHANG, Wei YANG, Weijie LI, Xiaoqi YANG, Yongxiang LIU. Classification of radar clutter amplitude statistical model based on complex-valued convolutional-ResNet [J]. Systems Engineering and Electronics, 2021, 43(11): 3086-3097.
[5]	Wang LU, Yasheng ZHANG, Can XU, Caiyong LIN. HRRP target recognition method based on bispectrum-spectrogram feature and deep convolutional neural network [J]. Systems Engineering and Electronics, 2020, 42(8): 1703-1709.
[6]	Qian XIANG, Xiaodan WANG, Rui LI, Jie LAI, Guoling ZHANG. HRRP image recognition of midcourse ballistic targets based on DCNN [J]. Systems Engineering and Electronics, 2020, 42(11): 2426-2433.
[7]	LIU Dai, ZHAO Yongbo, ZHOU Yongwei, CHEN Mingzhe, LI Wei. Maneuvering target tracking algorithm aided by a high resolution range profile [J]. Systems Engineering and Electronics, 2019, 41(9): 1967-1972.
[8]	WANG Caiyun, HUANG Panpan, LI Xiaofei, WANG Jianing, ZHAO Huanyue. Radar HRRP target recognition based on AEPSO-SVM algorithm [J]. Systems Engineering and Electronics, 2019, 41(9): 1984-1989.
[9]	HU Yangguang, XIAO Mingqing, ZHANG Kai, WANG Xiaozhu, DUAN Yaoze. Infrared aerial target tracking based on fusion of traditional feature and deep feature [J]. Systems Engineering and Electronics, 2019, 41(12): 2675-2683.
[10]	GUO Pengcheng, LIU Zheng, LUO Dingli. Radar HRRP clutter robust target recognition method based on double anomaly detection [J]. Systems Engineering and Electronics, 2019, 41(10): 2221-2226.
[11]	YU Sixuan, WANG Huawei. Risk forecasting in general aviation based on sparse de-noising auto-encoder neural network [J]. Systems Engineering and Electronics, 2019, 41(1): 112-117.
[12]	YUAN Jiawen, LIU Wenbo, ZHANG Gong. Application of dictionary learning algorithm in HRRP based on statistical modeling [J]. Systems Engineering and Electronics, 2018, 40(4): 762-767.
[13]	LI Bin, LI Hui. HRRP feature extraction based on mixtures of probabilistic principal component analysis [J]. Systems Engineering and Electronics, 2017, 39(1): 1-7.
[14]	WU Jia-ni, CHEN Yong-guang, FENG De-jun, WANG Xue-song. Target recognition for polarimetric HRRP based on pre-classification and model matching [J]. Systems Engineering and Electronics, 2016, 38(9): 1969-1974.
[15]	NING Chao, HUANG Jing, HUANG Pei-kang. Solution for characteristic parameters of precession cone-shaped #br# target using HRRP [J]. Systems Engineering and Electronics, 2014, 36(4): 650-655.

Correlation research between deep features of HRRP sparse auto-encoder and scattering center features

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 22

References 21

Related Articles 15

Recommended Articles

Metrics

Comments