前视扫描SAR超分辨成像

doi:10.3969/j.issn.1001-2400.2012.05.014

Abstract

Abstract:

A novel algorithm for super-resolution imaging based on the Compressive sensing theory for forward-looking SAR is proposed. Azimuth resolution is achieved by laying the practical linear array in a Multiple-Input and Multiple-Output mode. A narrow radiation pattern could be obtained using the beam-form technique to observe the small scene, which provides the sparsity prior of the SAR image. Then regularization is constructed by exploiting the sparsity prior and super-resolution could be realized by solving the optimization. Enhanced imaging resolution could be captured with a limited length of array, lowing the cost and complexity of the system. Numeric simulation results confirm the validation of the proposed algorithm.

Key words: forward-looking SAR, super-resolution, compressive sensing, sparsity

XU Gang;CHEN Qianqian;HOU Yuxing;LI Yachao;XING Mengdao. Super-resolution imaging of forward-looking scan SAR[J].J4, 2012, 39(5): 79-84+95.

References

［1］ Petillot I, Trouve E, Bolon P, et al. Radar-coding and Geocoding Lookup Tables for the Gusion of GIS and SAR Data In Mountain Areas［J］. IEEE Trans on Geoscience and Remote Sensing Letters, 2010, 7(2): 309-313.
［2］ Ren X Z, Tan L L, Yang R L. Research of Three-dimensional Imaging Processing for Airborne Forward-looking SAR［J］. IET International on Radar Conference, 2009(20-22): 1-4.
［3］ Hai P H, Chang W Q, Qiang Z, et al. Signal Analysis of Downward-looking and Forward-looking Array FMCW SAR［C］//International Congress on Image and Signal Processing(CISP). Yantai: IEEE, 2010: 4267-4270.
［4］ Ji S H, Ya X, Lawrence C. Bayesian Compressive Sensing［J］. IEEE Trans on Signal Processing, 2008, 56(6): 2346-2356.
［5］ Tello A M, López D P. A Novel Strategy for Radar Imaging Based on Compressive Sensing［J］. IEEE Trans on Geoscience and Remote Sensing, 2010, 48(12): 4285-4295.
［6］ Hong X B, Xia B, Ran T. A Novel Algorithm for Synthetic Aperture Radarimaging Based on Compressed Sensing［C］//IEEE 10th International Conference on Signal Processing (ICSP). Beijing: IEEE, 2010: 210-213.
［7］ Cetin M, Kard W C. Feature-enhanced Sunthetic Aperture Radar Image Formation Based on Nonquadratic Regularization［J］. IEEE Trans on Image Processing, 2001, 10(4): 623-631.
［8］ Zhang L, Xing M D, Qiu C W, et al. Achieving Higher Resolution ISAR Imaging with Limited Pluses via Compressed Sampling［J］. IEEE Trans on Geoscience and Remote Sensing Letters, 2009, 6(3): 567-571.
［9］ Berger S D. Nonuniform Sampling Reconstruction Applied to Sparse Array Beamforming［C］//IEEE Radar Conference on Proceedings. California: IEEE, 2002: 98-103.
［10］李真芳. 分布式小卫星SAR-InSAR-GMTI的处理方法［D］. 西安: 西安电子科技大学, 2006.
［11］ Sanadi S, Cetin M, Masnadi-Shirazi M A. Sparse Signal Representation for Complex-Valued Imaging［J］. Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009, 4(7): 365-370.
［12］ Zhang L, Xing M D, Qiu C W, et al. Resolution Enhancement for Inversed Synthetic Aperture Radar Imaging Under Low SNR via Improved Compressive Sensing［J］. IEEE Trans on Geoscience and Remote Sensing, 2010, 48(10): 3824-3838.

[1]	ZHANG Yuhao,CHENG Peitao,ZHANG Shuhao,WANG Xiumei. Lightweight image super-resolution with the adaptive weight learning network [J]. Journal of Xidian University, 2021, 48(5): 15-22.
[2]	ZHAN Keyu,SUN Yue,LI Ying. Video super-resolution based on multi-scale 3D convolution [J]. Journal of Xidian University, 2021, 48(5): 8-14.
[3]	LIU Shudong,WANG Xiaomin,ZHANG Yan. Symmetric residual convolution neural networks for the image super-resolution reconstruction [J]. Journal of Xidian University, 2019, 46(5): 15-23.
[4]	CHEN Baojia,HE Wangpeng,HU Jie,WANG Geng,GUO Baolong. Method for diagnosis of data-driven GMC sparse enhancement [J]. Journal of Xidian University, 2019, 46(4): 74-79.
[5]	CHEN Xiaofan,SHEN Haijie,BIAN Qian,WANG Zhenduo,TIAN Xinzhi. Face image super-resolution with an attention mechanism [J]. Journal of Xidian University, 2019, 46(3): 148-153.
[6]	SHI Wenjun,GUO Congzhou,TONG Xiaochong,TIAN Yuan,CAO Wen. Super-resolution reconstruction of infrared remote sensing images with radiation fidelity [J]. Journal of Xidian University, 2019, 46(2): 107-113.
[7]	SUN Chen, CHENG Liye. Spatial sensing matrix learning for PolSAR image classification [J]. Journal of Xidian University, 2018, 45(6): 92-98.
[8]	WANG Shiping;BI Duyan;LIU Kun;HE Linyuan. Multi-mapping convolution neural network for the image super-resolution algorithm [J]. Journal of Xidian University, 2018, 45(4): 155-160.
[9]	LI Wei;DENG Weibo;YANG Qiang;YSUO Ying;MIGLIORE Marco Donald. Diagnosis method for defective array elements based on compressive sensing [J]. Journal of Xidian University, 2018, 45(2): 160-165.
[10]	LIU Xuewen;XIAO Song;XUE Xiao. Measurement matrix construction based on empirical mode decomposition [J]. Journal of Xidian University, 2018, 45(1): 35-41.
[11]	LI Yanlong;CHEN Xiao;ZHAN Deman;WANG Junyi. Method of sparse multi-user detection in non-orthogonal multiple access [J]. Journal of Xidian University, 2017, 44(3): 151-156.
[12]	YANG Wei;LIU Hongqing;LI Yong;ZHOU Yi. Joint estimation algorithms based on LMS and RLS in the presence of impulsive noise [J]. Journal of Xidian University, 2017, 44(2): 165-170.
[13]	QUAN Lei;XIAO Song;XUE Xiao;LI Ying. Fast sensing method in compressive sensing with low complexity [J]. Journal of Xidian University, 2017, 44(1): 106-111.
[14]	JING Guobin;SHENG Jialian;CHEN Jianlai;SUN Guangcai;XING Mengdao;BAO Zheng. Super-resolution imaging method for the SAR target in a strong clutter scene [J]. Journal of Xidian University, 2016, 43(5): 12-17+87.
[15]	WANG Yong;QIAO Qianqian;YANG Xiaoyu;XU Wenjuan;JIA Zheng;CHEN Chuchu;GAO Quanxue. Sparse Bayesian reconstruction combined with self-adaptive dictionary learning [J]. Journal of Xidian University, 2016, 43(4): 1-4+122.

Super-resolution imaging of forward-looking scan SAR

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 10