结合字典学习技术的ISAR稀疏成像方法

胡长雨; 汪玲; 朱栋强

doi:10.11999/JEIT180747

结合字典学习技术的ISAR稀疏成像方法

doi: 10.11999/JEIT180747

南京航空航天大学雷达成像与微波光子技术教育部重点实验室南京 210016

基金项目: 国家自然科学基金(61871217)，江苏省研究生科研与实践创新计划(KYCX18_0291)

详细信息

作者简介:
胡长雨：男，1989年生，博士生，研究方向为逆合成孔径雷达稀疏成像

汪玲：女，1977年生，教授，博士生导师，研究方向为合成孔径成像、逆合成孔径成像、无源成像、压缩感知成像和超分辨成像

朱栋强：男，1993年生，硕士生，研究方向为基于压缩感知的ISAR成像

通讯作者:
汪玲　tulip_wling@nuaa.edu.cn

中图分类号: TN957.52
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- 被引次数: 0
出版历程
- 收稿日期: 2018-07-23
- 修回日期: 2019-01-21
- 网络出版日期: 2019-02-14
- 刊出日期: 2019-07-01

Sparse ISAR Imaging Exploiting Dictionary Learning

Key Laboratory of Radar Imaging and Microwave Photonics of the Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Funds: The National Natural Science Foundation of China (61871217), The Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX18_0291)

摘要

摘要: 鉴于稀疏ISAR成像方法的成像质量受到待成像场景的稀疏表示不准确的限制，该文将字典学习(DL)技术引入到ISAR稀疏成像中，以提升目标成像质量。该文给出基于离线DL和在线DL两种ISAR稀疏成像方法。前者通过已有同类目标ISAR图像进行学习，获得更优稀疏表示，后者在成像过程中从现有数据中通过优化获得稀疏表示。仿真和实测ISAR数据成像结果表明，结合离线DL和在线DL的成像方法均可获得比现有方法更优的成像结果，离线DL成像优于在线DL成像，而且前者计算效率优于后者。
- 逆合成孔径雷达 /
- 稀疏成像 /
- 稀疏表示 /
- 字典学习 /
- 压缩感知
Abstract: In view of the imaging quality of sparse ISAR imaging methods is limited by the inaccurate sparse representation of the scene to be imaged, the Dictionary Learning (DL) technique is introduced into ISAR sparse imaging to get better sparse representation of the scene. An off-line DL based imaging method and an on-line DL based imaging method are proposed. The off-line DL imaging method can obtain a better sparse representation via a dictionary learned from the available ISAR images. The on-line DL imaging method can obtain the sparse representation from the data currently considered by jointly optimizing the imaging and DL processes. The results of both simulated and real ISAR data show that the on-line DL imaging method and the off-line dictionary imaging method are both able to better sparsely represent the target scene leading to better imaging results. The off-line DL based imaging method works better than the on-line DL based imaging method with respect to both imaging quality and computational efficiency.
- Inverse Synthetic Aperture Radar (ISAR) /
- Sparse imaging /
- Sparse representation /
- Dictionary Learning(DL) /
- Compressive Sensing (CS)

HTML全文

图 1 飞机目标全数据采用RD方法和50%数据采用CS 方法的成像结果

下载: 全尺寸图片幻灯片

图 2 卫星目标全数据采用RD方法和25%数据采用CS方法的成像结果

下载: 全尺寸图片幻灯片

表 1 飞机目标成像性能评价

成像方法	FA	MD	RRMSE	TCR	ENT	IC	运算时间(s)
OMP	89	165	0.1923	57.0203	5.4631	8.0294	116.1757
GKF	86	103	0.2044	55.5930	5.3800	8.1449	1.0058e3
在线DL	74	75	0.1535	57.5629	5.3807	8.2103	52.5790
离线DL	64	70	0.1411	59.0322	5.3685	8.2868	24.8510

下载: 导出CSV

表 2 卫星目标成像性能评价

成像方法	FA	MD	RRMSE	TCR	ENT	IC	运算时间(s)
OMP	146	507	0.3736	63.2956	6.4209	9.8099	56.1323
GKF	140	478	0.2550	65.3382	6.3740	10.3843	1.6485e4
在线DL	142	161	0.1765	65.9163	6.6098	9.5039	19.2178
离线DL	122	147	0.1564	67.2506	6.6137	9.6094	4.1543

下载: 导出CSV

参考文献(24)

PRICKETT M J and CHEN C C. Principles of inverse synthetic aperture radar /ISAR/ imaging[C]. IEEE Electronics and Aerospace Systems Conference, New York, USA, 1980: 340–345.

GENG Minming, TIAN Ye, FANG Jian, et al. Implementation of GPU-based iterative shrinkage-thresholding algorithm in sparse microwave imaging[C]. Proceedings of 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 2012: 3863–3866.

CETIN M and KARL W C. Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization[J]. IEEE Transactions on Image Processing, 2001, 10(4): 623–631. doi: 10.1109/83.913596

汪玲, 朱栋强, 马凯莉, 等. 空间目标卡尔曼滤波稀疏成像方法[J]. 电子与信息学报, 2018, 40(4): 846–852. doi: 10.11999/JEIT170319

WANG Ling, ZHU Dongqiang, MA Kaili, et al. Sparse imaging of space targets using Kalman filter[J]. Journal of Electronics &Information Technology, 2018, 40(4): 846–852. doi: 10.11999/JEIT170319

徐宗本, 吴一戎, 张冰尘, 等. 基于L1/2正则化理论的稀疏雷达成像[J]. 科学通报, 2018, 63(14): 1306–1319. doi: 10.1360/N972018-00372

XU Zongben, WU Yirong, ZHANG Bingchen, et al. Sparse radar imaging based on L1/2 regularization theory[J]. Chinese Science Bulletin, 2018, 63(14): 1306–1319. doi: 10.1360/N972018-00372

HASANKHAN M J, SAMADI S, and ÇETIN M. Sparse representation-based algorithm for joint SAR image formation and autofocus[J]. Signal, Image and Video Processing, 2017, 11(4): 589–596. doi: 10.1007/s11760-016-0998-y

BI Hui, BI Guoan, ZHANG Bingchen, et al. Complex-image-based sparse SAR imaging and its equivalence[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(9): 5006–5014. doi: 10.1109/TGRS.2018.2803802

SAMADI S, CETIN M, and MASNADI-SHIRAZI M A. Sparse representation-based synthetic aperture radar imaging[J]. IET Radar, Sonar & Navigation, 2011, 5(2): 182–193. doi: 10.1049/iet-rsn.2009.0235

WANG Ling, LOFFELD O, MA Kaili, et al. Sparse ISAR imaging using a greedy kalman filtering approach[J]. Signal Processing, 2017, 138: 1–10. doi: 10.1016/j.sigpro.2017.03.002

DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306. doi: 10.1109/TIT.2006.871582

BARANIUK R and STEEGHS P. Compressive radar imaging[C]. Proceedings of 2007 IEEE Radar Conference, Boston, USA, 2007: 128–133.

WANG Lu, ZHAO Lifan, and BI Guoan. Structured sparse representation based ISAR imaging[C]. Proceedings of the 2014 15th International Radar Symposium, Gdansk, Poland, 2014: 1–5.

YANKELEVSKY Y and ELAD M. Dictionary learning for high dimensional graph signals[C]. Proceedings of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, Canada, 2018: 4669–4673.

YANKELEVSKY Y and ELAD M. Structure-aware classification using supervised dictionary learning[C]. Proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing, New Orleans, USA, 2017: 4421–4425.

SOĞANLUI A and ÇETIN M. Dictionary learning for sparsity-driven SAR image reconstruction[C]. Proceedings of 2014 IEEE International Conference on Image Processing, Paris, France, 2014: 1693–1697.

JIANG Changhui, ZHANG Qiyang, FAN Rui, et al. Super-resolution CT image reconstruction based on dictionary learning and sparse representation[J]. Scientific Reports, 2018, 8(1): 8799. doi: 10.1038/s41598-018-27261-z

AHARON M, ELAD M, and BRUCKSTEIN A. rmK-SVD: An algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311–4322. doi: 10.1109/TSP.2006.881199

PATI Y C, REZAIIFAR R, and KRISHNAPRASAD P S. Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition[C]. Proceedings of the 27th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, USA, 1993: 40–44.

WANG Ling and LOFFELD O. ISAR imaging using a null space ℓ1minimizing Kalman filter approach[C]. Proceedings of the 2016 4th International Workshop on Compressed Sensing Theory and Its Applications to Radar, Sonar and Remote Sensing, Aachen, Germany, 2016: 232–236.

AHARON M and ELAD M. Sparse and redundant modeling of image content using an image-signature-dictionary[J]. SIAM Journal on Imaging Sciences, 2008, 1(3): 228–247. doi: 10.1137/07070156X

ZHU Daiyin, WANG Ling, YU Yusheng, et al. Robust ISAR range alignment via minimizing the entropy of the average range profile[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(2): 204–208. doi: 10.1109/LGRS.2008.2010562

汪玲, 朱岱寅, 朱兆达. 基于SAR实测数据的舰船成像研究[J]. 电子与信息学报, 2007, 29(2): 401–404.

WANG Ling, ZHU Daiyin, and ZHU Zhaoda. Study on ship imaging using SAR real data[J]. Journal of Electronics &Information Technology, 2007, 29(2): 401–404.

HU Changyu, WANG Ling, and LOFFELD O. Inverse synthetic aperture radar imaging exploiting dictionary learning[C]. Proceedings of 2018 IEEE Radar Conference, Oklahoma City, USA, 2018: 1084–1088.

LOFFELD O, ESPETER T, and CONDE M H. From weighted least squares estimation to sparse CS reconstruction[C]. Proceedings of the 2015 3rd International Workshop on Compressed Sensing Theory and Its Applications to Radar, Sonar and Remote Sensing, Pisa, Italy, 2015: 149–153.

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