基于部件级三维参数化电磁模型的SAR目标物理可解释识别方法

文贡坚; 马聪慧; 丁柏圆; 宋海波

doi:10.12000/JR20099

基于部件级三维参数化电磁模型的SAR目标物理可解释识别方法

DOI: 10.12000/JR20099

1.
国防科技大学电子科学学院自动目标识别重点实验室长沙 410073
2.
航天工程大学北京 101416
3.
解放军96901部队25分队北京 100094

基金项目: 国家部委基金

详细信息

作者简介:
文贡坚(1972–)，男，湖南宁乡人，教授，博士生导师，研究方向为遥感图像处理

马聪慧(1987–)，女，湖北襄阳人，博士，讲师。2017年在国防科技大学电子工程学院获得博士学位，现担任航天工程大学讲师。主要研究方向为SAR目标识别。E-mail: ma_conghui@yeah.net

丁柏圆(1990–)，男，安徽池州人，博士。2018年在国防科技大学电子工程学院获得博士学位，现为96901部队助理研究员。研究方向为SAR自动目标识别

宋海波(1992–)，男，内蒙古呼伦贝尔人，博士生。主要研究方向为SAR自动目标识别，特征提取

通讯作者:
文贡坚 wengongjian@sina.com

责任主编：邢孟道 Corresponding Editor: XING Mengdao
中图分类号: TN957
计量
- 文章访问数:
- HTML全文浏览量:
- PDF下载量:
- 被引次数: 0
出版历程
- 收稿日期: 2020-07-08
- 修回日期: 2020-08-19
- 网络出版日期: 2020-08-28

SAR Target Physics Interpretable Recognition Method Based on Three Dimensional Parametric Electromagnetic Part Model

1.
National Key Laboratory of Science and Technology on Automatic Target Recognition, College of Electronic Science, National University of Defense Technology, Changsha 410073, China
2.
Space Engineering University, Beijing 101416, China
3.
Unit 96901 of People’s Liberation Army, Beijing 100094, China

Funds: The National Minstries Foundation

More Information

Corresponding author: WEN Gongjian, wengongjian@sina.com

摘要

摘要: 该文通过部件级三维参数化电磁模型(3D-PEPM)描述了复杂目标的电磁散射现象，并基于此模型提出了一种新的合成孔径雷达(SAR)目标识别方法。该方法首先根据雷达参数将3D-PEPM中各个散射体的散射响应投影到二维图像平面，预测每个散射体的位置和形状，然后根据3D-PEPM提供的先验信息评估3D-PEPM与SAR数据之间的相似程度，最后利用一种视角调整方法对整个过程进行优化，产生3D-PEPM和SAR数据之间的最终匹配分数，并根据该匹配分数完成识别决策。这种识别方法明确标识了SAR数据和3D-PEPM散射体之间的对应关系，具有清晰的物理可解释性，能够有效处理各种扩展条件下的SAR目标识别问题，仿真实验验证了该方法的有效性。
- 合成孔径雷达 /
- 目标识别 /
- 部件级 /
- 三维参数化电磁模型 /
- 物理可解释性
Abstract: In this paper, a target’s electromagnetic scattering phenomenon is characterized by the Three Dimensional Parametric Electromagnetic Part Model (3D-PEPM) and a novel Synthetic Aperture Radar (SAR) target recognition method is proposed based on the model. The proposed method projects the individual scatterers in the 3D-PEPM to the 2D image plane to predict the location and appearance for each scatterer according to the radar parameters firstly. Then based on the prior information provided by the 3D-PEPM, the similarities between the 3D-PEPM and SAR data are evaluated. Finally, a view angle adjusting method is utilized to optimize the whole process to produce the final match score between the model and SAR data, and the recognition decision is made according to the match score. The proposed recognition method identifies clearly the correspondences of the scatterers between SAR data and 3D-PEPM and enjoys the explicit physical interpretability, so it can deal with SAR recognition problems under various extended operating conditions. Experiments on simulated data reveal the effectiveness of the proposed method.
- Synthetic Aperture Radar (SAR) /
- Target recognition /
- Part level /
- Three dimensional parametric electromagnetic model /
- Physics interpretable

HTML全文

图 1 基于3D-PEPM ATR框架的流程图

Figure 1. The flow chart based on 3D-PEPM ATR framework

下载: 全尺寸图片幻灯片

图 2 SAR成像几何

Figure 2. SAR imaging geometry

下载: 全尺寸图片幻灯片

图 3 几何和辐射校正阵列

Figure 3. Geometric and radiometric correction array

下载: 全尺寸图片幻灯片

图 4 相似度测量框架

Figure 4. Similarity measurement framework

下载: 全尺寸图片幻灯片

图 5 简易坦克的CAD模型

Figure 5. CAD model of the simple tank

下载: 全尺寸图片幻灯片

图 6 简易坦克中的散射体

Figure 6. Scatterers in the simple tank

下载: 全尺寸图片幻灯片

图 7 模型和EM仿真软件产生的目标RCS对比

Figure 7. Comparison of target RCS generated by 3D-PEPM and EM simulation software

下载: 全尺寸图片幻灯片

图 8 不同视角下基于3D-PEPM生成的图像

Figure 8. Images generated from different perspectives based on 3D-PEPM

下载: 全尺寸图片幻灯片

图 9 相同视角下3个模型的观测图像

Figure 9. Observation images of three models in the same perspective

下载: 全尺寸图片幻灯片

图 10 模型图像和3D-PEPM物理相关的散射体

Figure 10. Model image and 3D-PEPM physically related scatterers

下载: 全尺寸图片幻灯片

图 11 模型图像和3D-PEPM物理相关的散射体

Figure 11. Model image and 3D-PEPM physically related scatterers

下载: 全尺寸图片幻灯片

图 12 相同视角下3个模型的观测图像

Figure 12. Observation images of three models in the same perspective

下载: 全尺寸图片幻灯片

图 13 Slicy目标在不同信噪比下的仿真图像

Figure 13. The simulated images of the slicy target under different SNR

下载: 全尺寸图片幻灯片

图 14 不同噪声水平下的相似度

Figure 14. Similarity under different noise levels

下载: 全尺寸图片幻灯片

图 15 散射体遮挡下的相似度测量性能

Figure 15. Performance of similarity measurement under scatterer occlusion

下载: 全尺寸图片幻灯片

表 1 参数的统计模型

Table 1. Statistical model of parameters

特征属性	均值	方差
距离向位置 $x$	${x_0}$	${\sigma^2 _x} = {f^2_{ {\rm{Downrange} } } }$
方位向位置 $y$	${y_0}$	${\sigma^2 _y} = {f^2_{ {\rm{Crossrange} } } }$
幅度 $\log 10(\left\| A \right\|)$	$\lg ({\left\| A \right\|_0})$	${\sigma^2 _A} = 0.5$
长度 $L$	${L_0}$	${\sigma^2 _L} = {(2{f_{ {\rm{Crossrange} } } })^2}$

下载: 导出CSV

表 2 模型投影和从数据估计得到的散射体参数的对比

Table 2. Comparison between model projection and scatterer parameters estimated from data

序号	模型投影得到的散射体参数				从数据估计得到的散射体参数				相似度
序号	X(m)	Y(m)	L(m)	A	X(m)	Y(m)	L(m)	A	相似度
1	–1.932	0.567	4.416	1.000	–1.932	0.487	4.265	1.000	0.643
2	–0.901	1.772	0.649	0.532	–0.900	1.780	0	0.193	0.254
3	0.181	1.212	0.708	0.458	0.182	1.210	0.695	0.095	0.902
4	0.181	2.400	1.057	0.328	0.183	2.394	0.868	0.114	0.705
5	0.181	–1.126	0.668	0.091	0.178	–1.168	0.633	0.048	0.841
6	–1.601	0	0	0.067	–1.730	–0.001	0	0.076	0.664
7	–1.068	–3.031	4.377	0.052	–0.929	–2.879	4.276	0.044	0.351
8	–0.772	–1.264	0.647	0.010	–0.770	–1.262	0.697	0.018	0.922
9	–1.431	0	0	0.008	–1.548	–0.002	0	0.045	0.689
10	0.338	–2.598	0.325	0.006	0.340	–2.324	0	0.001	0.227

下载: 导出CSV

参考文献(34)

[1]	EL-DARYMLI K, GILL E W, MCGUIRE P, et al. Automatic target recognition in synthetic aperture radar imagery: A state-of-the-art review[J]. IEEE Access, 2016, 4: 6014–6058. doi: 10.1109/ACCESS.2016.2611492
[2]	NOVAK L M, OWIRKA G J, BROWER W S, et al. The automatic target-recognition system in SAIP[J]. The Lincoln Laboratory Journal, 1997, 10(2): 187–202.
[3]	PARK J I, PARK S H, and KIM K T. New discrimination features for SAR automatic target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(3): 476–480. doi: 10.1109/LGRS.2012.2210385
[4]	NICOLI L P and ANAGNOSTOPOULOS G C. Shape-based recognition of targets in synthetic aperture radar images using elliptical Fourier descriptors[C]. SPIE 6967, Automatic Target Recognition XVIII, Orlando, USA, 2008.
[5]	MISHRA A K. Validation of PCA and LDA for SAR ATR[C]. The TENCON 2008 - 2008 IEEE Region 10 Conference, Hyderabad, India, 2008: 1–6.
[6]	宦若虹, 杨汝良. 基于小波域NMF特征提取的SAR图像目标识别方法[J]. 电子与信息学报, 2009, 31(3): 588–591. doi: 10.3724/SP.J.1146.2007.01808 HUAN Ruohong and YANG Ruliang. Synthetic aperture radar images target recognition based on wavelet domain NMF feature extraction[J]. Journal of Electronics &Information Technology, 2009, 31(3): 588–591. doi: 10.3724/SP.J.1146.2007.01808
[7]	DONG Ganggang and KUANG Gangyao. Classification on the monogenic scale space: Application to target recognition in SAR image[J]. IEEE Transactions on Image Processing, 2015, 24(8): 2527–2539. doi: 10.1109/TIP.2015.2421440
[8]	POTTER L C and MOSES R L. Attributed scattering centers for SAR ATR[J]. IEEE Transactions on Image Processing, 1997, 6(1): 79–91. doi: 10.1109/83.552098
[9]	MOSES P L, POTTER L C, and GUPTA I J. Feature extraction using attributed scattering center models for model-based automatic target recognition (ATR)[R]. AFRL-SN-WP-TR-2006-1004, 2005.
[10]	LIU Xian, HUANG Yulin, PEI Jifang, et al. Sample discriminant analysis for SAR ATR[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(12): 2120–2124. doi: 10.1109/LGRS.2014.2321164
[11]	HUANG Yulin, PEI Jifang, YANG Jianyu, et al. Neighborhood geometric center scaling embedding for SAR ATR[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(1): 180–192. doi: 10.1109/TAES.2013.110769
[12]	DING Jun, CHEN Bo, LIU Hongwei, et al. Convolutional neural network with data augmentation for SAR target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 364–368.
[13]	CHEN Sizhe, WANG Haipeng, XU Feng, et al. Target classification using the deep convolutional networks for SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8): 4806–4817. doi: 10.1109/TGRS.2016.2551720
[14]	ROSS T D, BRADLEY J J, HUDSON L J, et al. SAR ATR: So what’s the problem? An MSTAR perspective[C]. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, Orlando, USA, 1999: 606–610.
[15]	KEYDEL E R, LEE S W, and MOORE J T. MSTAR extended operating conditions: A tutorial[C]. SPIE 2757, Algorithms for Synthetic Aperture Radar Imagery III, Orlando, USA, 1996: 228–242.
[16]	JONES III G and BHANU B. Recognition of articulated and occluded objects[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(7): 603–613. doi: 10.1109/34.777371
[17]	DIEMUNSCH J R and WISSINGER J. Moving and stationary target acquisition and recognition (MSTAR) model-based automatic target recognition: Search technology for a robust ATR[C]. SPIE 3370, Algorithms for Synthetic Aperture Radar Imagery V, Orlando, USA, 1998: 481–492.
[18]	HUANG Peikang, YIN Hongcheng, and XU Xiaojian. Radar Target Characteristics[M]. Beijing: Publishing House of Electronics Industry, 2006.
[19]	CHIANG H C, MOSES R L, and POTTER L C. Model-based Bayesian feature matching with application to synthetic aperture radar target recognition[J]. Pattern Recognition, 2001, 34(8): 1539–1553. doi: 10.1016/S0031-3203(00)00089-3
[20]	ZHOU Jianxiong, SHI Zhiguang, XIAO Cheng, et al. Automatic target recognition of SAR images based on global scattering center model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(10): 3713–3729. doi: 10.1109/TGRS.2011.2162526
[21]	JACKSON J A. Three-dimensional feature models for synthetic aperture radar and experiments in feature extraction[D]. [Ph. D. dissertation], Ohio State University, 2009.
[22]	文贡坚, 朱国强, 殷红成, 等. 基于三维电磁散射参数化模型的SAR目标识别方法[J]. 雷达学报, 2017, 6(2): 115–135. doi: 10.12000/JR17034 WEN Gongjian, ZHU Guoqiang, YIN Hongcheng, et al. SAR ATR based on 3D parametric electromagnetic scattering model[J]. Journal of Radars, 2017, 6(2): 115–135. doi: 10.12000/JR17034
[23]	GIUSTI E, MARTORELLA M, and CAPRIA A. Polarimetrically-persistent-scatterer-based automatic target recognition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11): 4588–4599. doi: 10.1109/TGRS.2011.2164804
[24]	TANG Tao and SU Yi. Object recognition based on feature matching of scattering centers in SAR imagery[C]. The 5th International Congress on Image and Signal Processing, Chongqing, China, 2012: 1073–1076.
[25]	MARTORELLA M, GIUSTI E, DEMI L, et al. Target recognition by means of polarimetric ISAR images[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(1): 225–239. doi: 10.1109/TAES.2011.5705672
[26]	SAVILLE M A, SAINI D K, and SMITH J. Commercial vehicle classification from spectrum parted linked image test-attributed synthetic aperture radar imagery[J]. IET Radar, Sonar & Navigation, 2016, 10(3): 569–576.
[27]	RICHARDS J A. Target model generation from multiple synthetic aperture radar image[D]. [Ph. D. dissertation], 1996, MIT.
[28]	HE Yang, HE Siyuan, ZHANG Yunhua, et al. A forward approach to establish parametric scattering center models for known complex radar targets applied to SAR ATR[J]. IEEE Transactions on Antennas and Propagation, 2014, 62(12): 6192–6205. doi: 10.1109/TAP.2014.2360700
[29]	ZHOU Jianxiong, SHI Zhiguang, and FU Qiang. Three-dimensional scattering center extraction based on wide aperture data at a single elevation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(3): 1638–1655. doi: 10.1109/TGRS.2014.2346509
[30]	VOICU L I, PATTON R, and MYLER H R. Multicriterion vehicle pose estimation for SAR ATR[C]. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, Orlando, USA, 1999: 3721.
[31]	CUI Xunxue, ZHANG Jichun, and ZHOU Pucheng. Hypothesis testing for target detection model in sensor networks[C]. The 7th International Conference on Fuzzy Systems and Knowledge Discovery, Yantai, China, 2010.
[32]	BOSE R. Lean CLEAN: Deconvolution algorithm for radar imaging of contiguous targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(3): 2190–2199. doi: 10.1109/TAES.2011.5937291
[33]	SHAFER G. A Mathematical Theory of Evidence[M]. Princeton: Princeton University Press, 1976.
[34]	FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D, et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627–1645. doi: 10.1109/TPAMI.2009.167