Submit Manuscript
Peer Review
Editor Work
| Citation: | Wei Shunjun, Tian Bokun, Zhang Xiaoling, Shi Jun. Compressed Sensing Linear Array SAR Autofocusing Imaging via Semi-definite Programming[J]. Journal of Radars, 2018, 7(6): 664-675. doi: 10.12000/JR17103
|
Compressed Sensing Linear Array SAR Autofocusing Imaging via Semi-definite Programming
DOI: 10.12000/JR17103
-
Abstract
Linear Array Synthetic Aperture Radar (LASAR) is a novel and promising radar imaging technique. In recent years, Compressed Sensing (CS) sparse recovery has been a research focus for high-resolution three-Dimensional (3-D) LASAR imaging. Compared with the traditional two-Dimensional (2-D) SAR imaging, LASAR suffers from many problems, including under-sampling data and multi-dimensional and higher-order phase errors due to its sparse Linear Array Antenna (LAA) and the joint 2-D motions of the platform and LAA. The conventional autofocusing methods of 2-D SAR may be not suitable for CS-based LASAR 3-D sparse autofocusing. To address the multi-dimensional and higher-order phase errors in LASAR 3-D imaging with respect to under-sampling data, in this paper, we propose a sparse autofocusing algorithm based on semi-definite programming for CS-based LASAR imaging. First, by combining CS-based imaging theory, image maximum sharpness, and the minimum square error principle, we construct a LASAR phase-error estimation model based on under-sampled data. Next, we use semi-definite programming relaxation to estimate the phase errors. Lastly, we employ an iterated approximation method to improve the precision of the phase-error estimation and achieve the final CS-based LASAR autofocusing. To further improve the efficiency of the algorithm, we select only the dominant scattering areas for LASAR phase-error estimation. We present our simulation and experimental results to confirm the effectiveness of out proposed algorithm. -
-
References
[1] Du L, Wang Y P, Hong W, et al. A three-dimensional range migration algorithm for downward-looking 3D-SAR with single-transmitting and multiple-receiving linear array antennas[J]. EURASIP Journal on Advances in Signal Processing, 2010, 2010: 957916. DOI: 10.1155/2010/957916[2] Liao K F, Zhang X L, and Shi J. Plane-wave synthesis and RCS extraction via 3-D linear array SAR[J]. IEEE Antennas and Wireless Propagation Letters, 2015, 14: 994–997. DOI: 10.1109/LAWP.2015.2389264[3] Han K Y, Wang Y P, Tan W X, et al. Efficient pseudopolar format algorithm for down-looking linear-array SAR 3-D imaging[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(3): 572–576. DOI: 10.1109/LGRS.2014.2351792[4] Zhang S Q, Zhu Y T, and Kuang G Y. Imaging of downward-looking linear array three-dimensional SAR based on FFT-MUSIC[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(4): 885–889. DOI: 10.1109/LGRS.2014.2365611[5] Wei S J, Zhang X L, and Shi J. Linear array SAR imaging via compressed sensing[J]. Progress In Electromagnetics Research, 2011, 117: 299–319. DOI: 10.2528/PIER11033105[6] Zhang S Q, Zhu Y T, Dong G G, et al. Truncated SVD-based compressive sensing for downward-looking three-dimensional SAR imaging with uniform/nonuniform linear array[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(9): 1853–1857. DOI: 10.1109/LGRS.2015.2431254[7] Zhang S Q, Dong G G, Kuang G Y, et al. Superresolution downward-looking linear array three-dimensional SAR imaging based on two-dimensional compressive sensing[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(6): 2184–2196. DOI: 10.1109/JSTARS.2016.2549548[8] Peng X M, Tan W X, Hong W, et al. Airborne DLSLA 3-D SAR image reconstruction by combination of polar formatting and L1 regularization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1): 213–226. DOI: 10.1109/TGRS.2015.2453202[9] Tian J H, Sun J P, Han X, et al.. Motion compensation for compressive sensing SAR imaging with autofocus[C]. Proceedings of the 6th IEEE Conference on Industrial Electronics and Applications (ICIEA), Beijing, China, 2011: 1564–1567. DOI: 10.1109/ICIEA.2011.5975839.[10] Cetin M, Stojanovic I, Onhon O, et al. Sparsity-driven synthetic aperture radar imaging: Reconstruction, autofocusing, moving targets, and compressed sensing[J]. IEEE Signal Processing Magazine, 2014, 31(4): 27–40. DOI: 10.1109/MSP.2014.2312834[11] Onhon N Ö and Cetin M. A sparsity-driven approach for joint SAR imaging and phase error correction[J]. IEEE Transactions on Image Processing, 2012, 21(4): 2075–2088. DOI: 10.1109/TIP.2011.2179056[12] Zhe Z, Yao Z, Jiang C L, et al.. Autofocus of sparse microwave imaging radar based on phase recovery[C]. Proceedings of 2013 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC), Kunming, China, 2013: 1–5. DOI: 10.1109/ICSPCC.2013.6663989.[13] Chen Y C, Li G, Zhang Q, et al. Motion compensation for airborne SAR via parametric sparse representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(1): 551–562. DOI: 10.1109/TGRS.2016.2611522[14] Camlica S, Gurbuz A C, Arikan O, et al. Autofocused spotlight SAR image reconstruction of off-grid sparse scenes[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(4): 1880–1892. DOI: 10.1109/TAES.2017.2675138[15] Uḡur S and Arıkan O. SAR image reconstruction and autofocus by compressed sensing[J]. Digital Signal Processing, 2012, 22(6): 923–932. DOI: 10.1016/j.dsp.2012.07.011[16] Kelly S, Yaghoobi M, and Davies M. Sparsity-based autofocus for undersampled synthetic aperture radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 972–986. DOI: 10.1109/TAES.2014.120502[17] Uḡur S, Arıkan O, and Gürbüz A C. Off-grid sparse SAR image reconstruction by EMMP algorithm[C]. Proceedings of 2013 IEEE Radar Conference (RADAR), Ottawa, ON, Canada, 2013: 1–4. DOI: 10.1109/RADAR.2013.6586034.[18] Wei S J and Zhang X L. Sparse autofocus recovery for under-sampled linear array SAR 3-D imaging[J]. Progress In Electromagnetics Research, 2013, 140: 43–62. DOI: 10.2528/PIER13020614[19] Wei S J, Zhang X L, and Shi J. Sparse autofocus via Bayesian learning iterative maximum and applied for LASAR 3-D imaging[C]. Proceedings of 2014 IEEE Radar Conference, Cincinnati, OH, USA, 2014: 666–669. DOI: 10.1109/RADAR.2014.6875674.[20] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306. DOI: 10.1109/TIT.2006.871582[21] Figueiredo M A T, Nowak R D, and Wright S J. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems[J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4): 586–597. DOI: 10.1109/JSTSP.2007.910281[22] Ji S H, Xue Y, and Carin L. Bayesian compressive sensing[J]. IEEE Transactions on Signal Processing, 2008, 56(6): 2346–2356. DOI: 10.1109/TSP.2007.914345[23] Grant M and Boyd S. CVX: Matlab software for disciplined convex programming, version 1.21[R]. CVX Research, Inc., 2010. Available from: URL: http://cvxr.com/cvx.[24] Toh K C, Todd M J, and Tütüncü R H. SDPT3—A Matlab software package for semidefinite programming, version 1.3[J]. Optimization Methods and Software, 1999, 11(1/4): 545–581. DOI: 10.1080/10556789908805762[25] Liu K H, Wiesel A, and Munson D C. Synthetic aperture radar autofocus via semidefinite relaxation[J]. IEEE Transactions on Image Processing, 2013, 22(6): 2317–2326. DOI: 10.1109/TIP.2013.2249084 -
Proportional views
- Publishing Ethics
- Journal Insights
- Abstracting & Indexing
- Peer Review Policies
- Guide for Authors
- Conference
- ISSN 2095-283X (Print)ISSN 2097-339X (Online)
- CN 10-1030/TN
- CODEN LXEUAO
About Journal
- Sponsor: China Radio Detection and Ranging Industry Association (CRIA)
- Phone: 010-58887062
- Email：radars@aircas.ac.cn
- Publisher: Leida Xuebao Bianjibu (Editorial office of the Journal of Radars)
Contacts Us
京ICP备20021838号-8
Supported by: Beijing Renhe Information Technology Co. Ltd
Export File
Citation
Format
Content
DownLoad:
- Figure 1. The geographic model of down-looking LASAR imaging
- Figure 3. The results of the point targets in the case of the along-track random phase errors and cross-track random phase errors(Top: all samples; Middle: 50% samples; Bottom: 25% samples)
- Figure 4. The MSE curve of SDPSA
- Figure 2. The results of the point targets in the case of the along-track quadratic phase errors and cross-track random phase errors(Top: all samples; Middle: 50% samples; Bottom: 25% samples)
- Figure 5. The ground-based LASAR experiment
- Figure 6. The virtual array antenna and the echo of the ground-based LSAR
- Figure 7.
- Figure 8. The sparse autofocusing results of the experimental light data(Left: BP-PGA; Middle: IRLS-PGA; Right: SDPSA)