SAR target physics interpretable recognition method based on three dimensional parametric electromagnetic part model

被引：0
|
作者：
Wen G. [1 ]
Ma C. [2 ]
Ding B. [3 ]
Song H. [1 ]
机构：
[1] National Key Laboratory of Science and Technology on Automatic Target Recognition, College of Electronic Science, National University of Defense Technology, Changsha
[2] Space Engineering University, Beijing
[3] Unit 96901 of People’s Liberation Army, Beijing
来源：
Wen, Gongjian (wengongjian@sina.com) | 1600年 / Institute of Electronics Chinese Academy of Sciences卷 / 09期
关键词：
Part level; Physics interpretable; Synthetic Aperture Radar (SAR); Target recognition; Three dimensional parametric electromagnetic model;
D O I：
10.12000/JR20099
中图分类号：
学科分类号：
摘要：
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. © 2020 Institute of Electronics Chinese Academy of Sciences. All rights reserved.
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页码：608 / 621
页数：13
相关论文
共 34 条
  • [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, 4, pp. 6014-6058, (2016)
  • [2] NOVAK L M, OWIRKA G J, BROWER W S, Et al., The automatic target-recognition system in SAIP[J], The Lincoln Laboratory Journal, 10, 2, pp. 187-202, (1997)
  • [3] PARK J I, PARK S H, KIM K T., New discrimination features for SAR automatic target recognition[J], IEEE Geoscience and Remote Sensing Letters, 10, 3, pp. 476-480, (2013)
  • [4] NICOLI L P, ANAGNOSTOPOULOS G C., Shape-based recognition of targets in synthetic aperture radar images using elliptical Fourier descriptors, SPIE 6967, Automatic Target Recognition XVIII, (2008)
  • [5] MISHRA A K., Validation of PCA and LDA for SAR ATR, The TENCON 2008 - 2008 IEEE Region 10 Conference, pp. 1-6, (2008)
  • [6] HUAN Ruohong, YANG Ruliang, Synthetic aperture radar images target recognition based on wavelet domain NMF feature extraction[J], Journal of Electronics & Information Technology, 31, 3, pp. 588-591, (2009)
  • [7] DONG Ganggang, KUANG Gangyao, Classification on the monogenic scale space: Application to target recognition in SAR image[J], IEEE Transactions on Image Processing, 24, 8, pp. 2527-2539, (2015)
  • [8] POTTER L C, MOSES R L., Attributed scattering centers for SAR ATR[J], IEEE Transactions on Image Processing, 6, 1, pp. 79-91, (1997)
  • [9] MOSES P L, POTTER L C, GUPTA I J., Feature extraction using attributed scattering center models for model-based automatic target recognition (ATR), AFRLSN-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, 11, 12, pp. 2120-2124, (2014)
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