基于数值散射模拟与模型匹配的SAR自动目标识别研究

周雨; 王海鹏; 陈思喆

doi:10.12000/JR15080

基于数值散射模拟与模型匹配的SAR自动目标识别研究

DOI: 10.12000/JR15080

(复旦大学电磁波信息科学教育部重点实验室, 上海 200433)

基金项目:

国家自然科学基金(61571132, 61331020)

详细信息

通讯作者:
王海鹏

计量
- 文章访问数: 2775
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- 被引次数: 0
出版历程
- 收稿日期: 2015-06-29
- 修回日期: 2015-10-14
- 网络出版日期: 2015-12-28

SAR Automatic Target Recognition Based on Numerical Scattering Simulation and Model-based Matching

(Key Laboratory for Information Science of Electromagnetic Waves, Fudan University, Shanghai 200433, China)

Funds:

The National Natural Science Foundation of China (61571132, 61331020)

摘要

摘要: 该文提出并实现了一种基于模型的SAR自动目标识别算法,该算法用实验室开发的BART进行离线电磁散射计算,系统参数设置和MSTAR数据库的参数完全一致,对待测图像和电磁散射数据所成的图像分别进行特征提取,然后进行搜索匹配。该文通过MSTAR 3类目标3种型号的实测数据和BART仿真数据分别验证了算法的可行性和准确性。该算法简单易实现,运行时间短,目标分类识别的效果较好。
- 合成孔径雷达 /
- 自动目标识别 /
- SAR图像模拟 /
- 模型匹配
Abstract: This study proposes a model-based Synthetic Aperture Radar (SAR) automatic target recognition algorithm. Scattering is computed offline using the laboratory-developed Bidirectional Analytic Ray Tracing software and the same system parameter settings as the Moving and Stationary Target Acquisition and Recognition (MSTAR) datasets. SAR images are then created by simulated electromagnetic scattering data. Shape features are extracted from the measured and simulated images, and then, matches are searched. The algorithm is verified using three types of targets from MSTAR data and simulated SAR images, and it is shown that the proposed approach is fast and easy to implement with high accuracy.
- Synthetic Aperture Radar (SAR) /
- Automatic Target Recognition (ATR) /
- SAR image simulation /
- Model-based

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参考文献(12)

[1]	张红, 王超, 张波, 等. 高分辨率SAR 图像目标识别[M]. 北京：科学出版社, 2009: 4-7. Zhang Hong, Wang Chao, Zhang Bo, et al.. High-Resolution SAR Image Target Recognition[M]. Beijing: Science Press, 2009: 4-7.
[2]	Moreira A, Prats-Iraola P, Younis M, et al.. A tutorial on synthetic aperture radar[J]. IEEE Geoscience and Remote Sensing Magazine, 2013, 1(1): 6-43.
[3]	程肖. 基于散射中心模型的 SAR 图像自动目标识别[D]. [硕士论文], 国防科学技术大学, 2009. Cheng Xiao. SAR ATR algorithm based on the scattering center model[D]. [Master dissertation], National University of Defense Technology, 2009.
[4]	裴季方. 基于邻域判别嵌入的SAR 自动目标识别研究[D]. [硕士论文], 电子科技大学, 2013. Pei Ji-fang. SAR ATR research based on neighborhood judging embedded method[D]. [Master dissertation], University of Electronic Science and Technology of China, 2013.
[5]	Zhou J, Shi Z, Cheng X, 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.
[6]	张锐, 洪峻, 明峰. 基于目标CSAR回波模型的SAR自动目标识别算法[J]. 电子与信息学报, 2011, 33(1): 27-32. Zhang Rui, Hong Jun, and Ming Feng. SAR ATR algorithm based on the CSAR echo model[J]. Journal of Electronics Information Technology, 2011, 33(1): 27-32.
[7]	Chiang H C, Moses R L, and Potter L C. Model-based classification of radar images[J]. IEEE Transactions on Information Theory, 2000, 46(5): 1842-1854.
[8]	Jones G and Bhanu B. Recognition of articulated and occluded objects[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(7): 603-613.
[9]	Boshra M and Bhanu B. Predicting performance of object recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(9): 956-969.
[10]	Xu F and Jin Y Q. Bidirectional analytic ray tracing for fast computation of composite scattering from electric-large target over a randomly rough surface[J]. IEEE Transactions on Antennas and Propagation, 2009, 57(5): 1495-1505.
[11]	Wissinger J, Ristroph R, Diemunsch J R, et al.. MSTARs extensible search engine and model-based inferencing toolkit[C]. Proceedings SPIE Algorithms for Synthetic Aperture Radar Imagery VI, 1999, 3721: 554-570.
[12]	Ross T D, Worrell S W, Velten V J, et al.. Standard SAR ATR evaluation experiments using the MSTAR public release data set[C]. Proceedings SPIE Algorithms for Synthetic Aperture Radar Imagery V, 1998, 3370: 566-573.