Abstract
This paper provides principles and applications of the sparse microwave imaging theory and technology. Synthetic aperture radar (SAR) is an important method of modern remote sensing. During decades microwave imaging technology has achieved remarkable progress in the system performance of microwave imaging technology, and at the same time encountered increasing complexity in system implementation. The sparse microwave imaging introduces the sparse signal processing theory to radar imaging to obtain new theory, new system and new methodology of microwave imaging. Based on classical SAR imaging model and fundamental theories of sparse signal processing, we can derive the model of sparse microwave imaging, which is a sparse measurement and recovery problem and can be solved with various algorithms. There exist several fundamental points that must be considered in the efforts of applying sparse signal processing to radar imaging, including sparse representation, measurement matrix construction, unambiguity reconstruction and performance evaluation. Based on these considerations, the sparse signal processing could be successfully applied to radar imaging, and achieve benefits in several aspects, including improvement of image quality, reduction of data amount for sparse scene and enhancement of system performance. The sparse signal processing has also been applied in several specific radar imaging applications.
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References
Curlander J C, McDonough R N. Synthetic Aperture Radar: Systems and Signal Processing. New York: Wiley, 1991
Henderson F M, Lewis A J. Principles and applications of imaging radar. In: Manual of Remote Sensing. New York: John Wiley and Sons, 1998
Wiley C, Ariz P. Pulsed Doppler Radar Methods and Apparatus. US Patent 3,196,436. 1965
Wiley C A. Synthetic aperture radars: a paradigm for technology evolution. IEEE Trans Aerosp Electron Syst, 1985: 21: 440–443
Wikipedia. Synthetic Aperture Radar. https://en.wikipedia.org/wiki/Synthetic_aperture_radar
NASA. Missions-Seasat 1. http://science.nasa.gov/missions/seasat-1/
Jordan R L. The Seasat-A synthetic aperture radar system. IEEE J Ocean Eng, 1980, 5: 154–164
CSA. CSA: RadarSat-1. http://www.asc-csa.gc.ca/eng/satellites/radarsat1/
DLR. TerraSAR-X-Germany’s radar eye in space. http://www.dlr.de/eo/en/desktopdefault.aspx/tabid-5725/9296_read-15979/
Jakowatz C V, Wahl D E, Eichel P H, et al. Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach. Norwell: Kluwer Academic Publishers, 1996
Massonnet D, Souyris J C. Imaging with Synthetic Aperture Radar. Lausanne: EFPL Press, 2008
Brown W M, Porcello L J. An introduction to synthetic-aperture radar. IEEE Spectrum, 1969, 6: 52–62
Sherwin C W, Ruina J P, Rawcliffe R D. Some early developments in synthetic aperture radar systems. IRE Trans Military Electron, 1962, 1051: 111–115
Moore G E. Cramming more components onto integrated circuits. Electron Mag, 1998, 86: 82–85
Woodward P M. Probability and Information Theory, with Application to Radar. New York: Pergamon, 1953
Cook C E, Bernfeld M. Radar Signals-An Introduction to Theory and Application. Norwood: Artech House, 1993
Nyquist H. Certain topics in telegraph transmission theory. Trans Amer Inst Electr Engin, 1928, 47: 617–644
Shannon C E. Communication in the presence of noise. Proc IRE, 1949, 37: 10–21
Baraniuk R G, Candès E, Elad M, et al. Applications of sparse representation and compressive sensing. Proc IEEE, 2010, 98: 906–09
Russell B. History of Western Philosophy. London: George Allen & Unwin Ltd, 1946
Donoho D L. Compressed Sensing. IEEE Trans Inf Theory, 2006. 52: 1289–1306
Candès E J, Tao T. Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans Inf Theory, 2006, 52: 5406–5425
Candès E J, Romberg J K, Tao T. Stable signal recovery from incomplete and inaccurate measurements. Commun Pure Appl Math, 2006, 59: 1207–1223
Mallat S, Yu G. Super-resolution with sparse mixing estimators. IEEE Trans Image Process, 2010, 19: 2889–2900
Zhang Y, Mei S, Chen Q, et al. A novel image/video coding method based on compressed sensing theory. In: Proc of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Las Vegas, 2008. 1361–1364
Berger C R, Zhou S, Preisig J, et al. Sparse channel estimation for multicarrier underwater acoustic communication: from subspace methods to compressed sensing. IEEE Trans Signal Process, 2010, 58: 1708–1721
Baraniuk R, Steeghs P. Compressive radar imaging. In: Proc of IEEE Radar Conference, Boston, 2007. 128–133
Patel V M, Easley G R, Healy D, et al. Compressed synthetic aperture radar. IEEE J Sel Top Signal Process, 2010, 4: 244–254
Ender J H G. On compressive sensing applied to radar. Signal Process, 2010, 90: 1402–1414
Wu Y R. Studies on theory, system, and methodology of Sparse Microwave Imaging. Statement Tasks and Project Plan of 973 Program: 2009
Cumming I G, Wong F H. Digital Signal Processing of Synthetic Aperture Radar Data: Algorithms and Implementation. Norwood: Artech House, 2004
Raney R K, Runge H, Bamler R, et al. Precision SAR processing using chirp scaling. IEEE Trans Geosci Remote Sens, 1994, 32: 786–799
Bamler R. A comparison of range-Doppler and wavenumber domain SAR focusing algorithms. IEEE Trans Geosci Remote Sens, 1992, 30: 706–713
Basu S, Bresler Y. O(N 2 log2 N) filtered backprojection reconstruction algorithm for tomography. IEEE Trans Image Process, 2000, 9: 1760–1773
Xiao S, Munson Jr D C, Basu S, et al. An N 2 logN back-projection algorithm for SAR image formation. In: Conference Record of Asilomar Conference on Signals, Systems and Computers (ACSSC), Pacific Grove, 2000
Suess M, Grafmüller B, Zahn R. A novel high resolution, wide swath SAR system.In: Proc of IEEE International Geoscience and Remoye Sensing Symposium (IGARSS), Sydney, 2001. 1013–1015
Suess M. Side-Looking Synthetic Aperture Radar System. European Patent 1,241,487. 2006
Currie A, Brown M A. Wide-swath SAR. IEE Proc F Radar Signal Process, 1992, 139: 122–135
Elad M. Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. New York: Springer, 2010
Mallat S G, Zhang Z. Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process, 1993, 41: 3397–3415
Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit. SIAM J Sci Comput, 1999, 20: 33–61
Candès E J, Tao T. Decoding by linear programming. IEEE Trans Inf Theory, 2005, 51: 4203–4215
Candès E, Tao T. The Dantzig selector: Statistical estimation when p is much larger than n. The Annal Stat, 2007, 35: 2313–2351
Candès E J, Romberg J, Tao T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory, 2006, 52: 489–509
Candès E, Romberg J. Sparsity and incoherence in compressive sampling. Inverse Problem, 2007, 23: 969–985
Tsaig Y, Donoho D L. Extensions of compressed sensing. Signal Process, 2006, 86: 549–571
Baraniuk R G. More is less: Signal processing and the data deluge. Science, 2011, 331: 717–719
Baron D, Wakin M B, Duarte M F, et al. Distributed Compressed Sensing. Technical Report. Rice: Rice University, 2005, http://dsp.rice.edu/cs/
Duarte M F, Sarvotham S, Baron D, et al. Distributed compressed sensing of jointly sparse signals. In: Conference Record of Asilomar Conference on Signals, Systems and Computers (ACSSC), Pacific Grove, 2005. 1537–1541
Zhang Z, Zhang B C, Hong W, et al. Waveform design for Lq regularization based radar imaging and an approach to radar imaging with non-moving platform. In: Proc of European Conference on Synthetic Aperture Radar (EuSAR), Nuremberg, 2012
Ahmed N, Natarajan T, Rao K R. Discrete cosine transform. IEEE Trans Comput, 1974, 100: 90–93
Daubechies I. Ten Lectures on Wavelets. Philadelphia: SIAM Publications, 2006
Ron A, Shen Z. Affine systems in L 2(d): the analysis of the Analysis operator. J Function Analys, 1997, 148: 408–447
Velisavljevic V, Dragotti P L, Vetterli M. Directional wavelet transforms and frames. In: Proc of Int Conf Image Processing, Rochester, 2002. 589–592
Candès E J. Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edges. Technical Report. DTIC Document, 2000. www.curvelet.org/papers/Curve99.pdf
Olshausen B A, Field D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 1996, 381: 607–609
Oliver C, Quegan S. Understanding Synthetic Aperture Radar Images. Raleigh: SciTech Publishing, 2004
Tian Y, Jiang C L, Lin Y G, et al. An evaluation method for sparse microwave imaging radar system using phase diagrams. In: Proc of CIE Radar Conference, Chengdu, 2011
Zhang B C, Jiang H, Hong W, et al. Synthetic aperture radar imaging of sparse targets via compressed sensing. In: Proc of 8th European Conference on Synthetic Aperture Radar (EUSAR), Aachen, 2010
Jiang H, Zhang B C, Lin Y G, et al. Random noise SAR based on compressed sensing. In: Proc of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Honolulu, 2010. 4624–4627
Shastry M C, Narayanan R M, Rangaswamy M. Compressive radar imaging using white stochastic waveforms. In: Proc of International Waveform Diversity and Design Conference (WDD), Niagara Falls, 2010. 90–94
Bahai A R S, Saltzberg B R, Ergen M. Multi-Carrier Digital Communications: Theory and Applications of OFDM. New York, NY: Springer Science and Business Media Inc, 2004
Berger C R, Zhou S, Willett P, et al. Compressed sensing for OFDM/MIMO radar. In: Conference Record of Asilomar Conference on Signals, Systems and Computers (ACSSC), Pacific Grove, 2008. 213–217
Berger C R, Demissie B, Heckenbach J, et al. Signal processing for passive radar using OFDM waveforms. IEEE J Sel Top Signal Process, 2010, 4: 226–238
Vetterli M, Marziliano P, Blu T. Sampling signals with finite rate of innovation. IEEE Trans Signal Process, 2002, 50: 1417–1428
Healy D. Analog-to-Information (A-to-I). 2005. http://www.darpa.mil/mto/solicitations/baa05-35/s/index.html. dARPA/MTO Broad Agency Announcement (BAA) #05-35
Healy D, Brady D J. Compression at the physical interface. IEEE Signal Process Mag, 2008, 25: 67–71
Laska J N, Kirolos S, Duarte M F, et al. Theory and implementation of an analog-to-information converter using random demodulation. In: Proc of IEEE International Symposium on Circuits and Systems (ISCAS), New Orleans, 2007. 1959–1962
Eldar Y C. Compressed Sensing of Analog Signals. Comput Res Reposit, 2008
Mishali M, Eldar Y C, Elron A. Xampling, Part I: Practice. ArXiv:09110519. 2009. http://arxiv.org/pdf/0911.0519v3
Mishali M, Eldar Y C. From theory to practice: Sub-Nyquist sampling of sparse wideband analog signals. IEEE J Sel Top Signal Process, 2010, 4: 375–391
Mishali M, Eldar Y, Dounaevsky O, et al. Xampling: Analog to digital at sub-Nyquist rates. IET Circ Dev Syst, 2011, 5: 8–20
Mishali M, Eldar Y C. Xampling: Compressed sensing of analog signals. ArXiv:11032960. 2011. http://arxiv.org/pdf/1103.2960
Tropp J A, Laska J N, Duarte M F, et al. Beyond Nyquist: Efficient sampling of sparse bandlimited signals. IEEE Trans Inf Theory, 2010, 56: 520–544
Balakrishnan A. On the problem of time jitter in sampling. IRE Trans Inform Theory, 1962, 8: 226–236
Sun J P, Zhang Y X, Chen Z B, et al. A novel spaceborne SAR wide-swath imaging approach based on Poisson disk-like nonuniform sampling and compressive sensing. Sci China Inf Sci, 2012, 55: 1876–1887
Candès E J, Wakin M B. An introduction to compressive sampling. IEEE Signal Process Mag, 2008, 25, 2: 21–30
Carrara W G, Goodman R S, Majewski R M. Spotlight Synthetic Aperture Radar-Signal Processing Algorithms. Norwood, 1995
Belcher D P, Baker C J. High resolution processing of hybrid strip-map/spotlight mode SAR. IEE Proc Radar Sonar Nav, 1996, 143: 366–374
Mittermayer J, Lord R, Borner E. Sliding spotlight SAR processing for TerraSAR-X using a new formulation of the extended chirp scaling algorithm. In: Proc of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, 2003. 1462–1464
De Zan F, Guarnieri A M. TOPSAR: Terrain observation by progressive scans. IEEE Trans Geosci Remote Sens, 2006, 44: 2352–2360
Tropp J A, Wright S J. Computational methods for sparse solution of linear inverse problems. Proc IEEE, 2010, 98: 948–958
Kim S J, Koh K, Lustig M, et al. An interior-point method for large-scale l1-regularized least squares. IEEE J Sel Top Signal Process, 2007, 1: 606–617
Candès E, Romberg J. l1-magic: Recovery of sparse signals via convex Programming. 2005. www.acm.caltech.edu/l1magic/downloads/l1magic.pdf
Figueiredo M A T, Nowak R D, Wright S J. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. IEEE J Sel Top Signal Process, 2007, 1: 586–597
Van Den Berg E, Friedlander M. Probing the Pareto frontier for basis pursuit solutions. SIAM J Sci Comput, 2008, 31: 890–912
Daubechies I, Defrise M, De Mol C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun Pure Appl Math, 2004, 57: 1413–1457
Bioucas-Dias J M, Figueiredo M A T. Two-step algorithms for linear inverse problems with non-quadratic regularization. In: Proc of IEEE Int Conf Image Processing, San Antonio, 2007
Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imag Sci, 2009, 2: 183–202
Yin W, Osher S, Goldfarb D, et al. Bregman iterative algorithms for L1-minimization with applications to compressed sensing. SIAM J Imag Sci, 2008, 1: 143–168
Hale E T, Yin W, Zhang Y. Fixed-point continuation for L1 minimization: methodology and convergence. SIAM J Optim, 2008: 1107–1130
Hale E T, Yin W, Zhang Y. Fixed-point continuation applied to compressed sensing: implementation and numerical experiments. J Comput Math, 2010, 28: 170–194
Becker S, Bobin J, Candès E. NESTA: A fast and accurate first-order method for sparse recovery. ArXiv: 09043367, 2009. http://arxiv.org/pdf/0904.3367
Becker S R, Candès E J, Grant M C. Templates for convex cone problems with applications to sparse signal recovery. Math Program Comput, 2011, 3: 165–218
Yang J, Zhang Y. Alternating direction algorithms for ℓ 1-problems in compressive sensing. ArXiv:09121185, 2009. http://arxiv.org/pdf/0912.1185
Lu Z, Pong T K, Zhang Y. An Method for Finding Dantzig Selectors. ArXiv: 10114604, 2010. http://arxiv.org/pdf/1011.4604
Davenport M A, Duarte M F, Eldar Y C, et al. Chapter I: Introduction to compressed sensing. In: Compressed Sensing: Theory and Applications. Cambridge: Cambridge University Press, 2012
Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory, 2007, 53: 4655–4666
Donoho D L, Drori I, Tsaig Y, et al. Sparse Solution of Underdetermined Linear Equations by Stagewise Orthogonal Matching Pursuit. Technical Report. Department of Statistics, Stanford University, 2006. http://nmetis.dk/pic/Speciale/HenrikPedersen/Henrik%20Pederse%ns%20Artikler/Compressed%20Sensing/Donoho.pdf
Blumensath T, Davies M E. Gradient pursuits. IEEE Trans Signal Process, 2008, 56: 2370–2382
Dai W, Milenkovic O. Subspace pursuit for compressive sensing signal reconstruction. IEEE Trans Inf Theory, 2009, 55: 2230–2249
Needell D, Vershynin R. Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit. IEEE J Sel Top Signal Process, 2010, 4: 310–316
Needell D, Tropp J A. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples. Appl Comput Harmon Analys, 2009, 26: 301–321
Blumensath T, Davies M E. Iterative hard thresholding for compressed sensing. Appl Comput Harmon Analys, 2009, 27: 265–274
Chartrand R. Exact reconstruction of sparse signals via nonconvex minimization. IEEE Signal Process Lett, 2007, 14: 707–710
Rao B D, Kreutz-Delgado K. An affine scaling methodology for best basis selection. IEEE Trans Signal Process, 1999, 47: 187–200
Chartrand R, Yin W. Iteratively reweighted algorithms for compressive sensing. In: Conference Record of Asilomar Conference on Signals, Systems and Computers (ACSSC), Pacific Grove, 2008. 3869–3872
Daubechies I, DeVore R, Fornasier M, et al. Iteratively reweighted least squares minimization for sparse recovery. Commun Pure Appl Math, 2010, 63: 1–38
Krishnan D, Fergus R. Fast image deconvolution using hyper-Laplacian priors. In: Advances in Neural Information Processing Systems (NIPS), 2009. 1033–1041
Xu Z B, Zhang H, Wang Y, et al. L 1/2 regularizer. Sci China Inf Sci, 2010, 53: 1159–1169
Wipf D P, Rao B D. Sparse Bayesian learning for basis selection. IEEE Trans Signal Process, 2004, 52: 2153–2164
Ji S, Xue Y, Carin L. Bayesian compressive sensing. IEEE Trans Signal Process, 2008, 56: 2346–2356
Ji S, Dunson D, Carin L. Multitask compressive sensing. IEEE Trans Signal Process, 2009, 57: 92–106
Babacan S D, Mancera L, Molina R, et al. Non-convex priors in Bayesian compressed sensing. In: Proc of 17th European Signal Processing Conference (EUSIPCO), Glasgow, 2009
Yoon Y S, Amin M G. Compressed sensing technique for high-resolution radar imaging. In: Proc SPIE, 2008. 6968
Stojanovic I, Karl W C, Çetin M. Compressed sensing of monostatic and multistatic SAR. In: Proc SPIE, 2009. 7337
Huang Q, Qu L, Wu B, et al. UWB through-wall imaging based on compressive sensing. IEEE Trans Geosci Remote Sens, 2010, 48: 1408–1415
Jiang C L, Jiang H, Zhang B C, et al. SNR analysis for SAR imaging from raw data via compressed sensing. In: Proc of European Conference on Synthetic Aperture Radar (EUSAR), Nuremberg, 2012
Austin C D, Ertin E, Moses R L. Sparse Multipass 3D SAR imaging: applications to the GOTCHA data set. In: Proc SPIE, 2009. 7337
Zhang L, Xing M, Qiu C W, et al. Resolution enhancement for inversed synthetic aperture radar imaging under low SNR via improved compressive sensing. IEEE Trans Geosci Remote Sens, 2010. 48,10: 3824–3838
Xie X C, Zhang Y H. High-resolution imaging of moving train by ground-based radar with compressive sensing. Electron Lett, 2010, 46: 529–531
Alonso M T, López-Dekker P, Mallorqui J J. A novel strategy for radar imaging based on compressive sensing. IEEE Trans Geosci Remote Sens, 2010, 48: 4285–4295
Wu Y R, Xu Z B, Hong W, et al. Sparse SAR Imaging Algorithm based on SAR Raw Data Simulator (in Chinese). China Patent, 201110182202.9. 2012
Andrecut M. Fast GPU implementation of sparse signal recovery from random projections. ArXiv: 08091833, 2008. http://arxiv.org/pdf/0809.1833
Borghi A, Darbon J, Peyronnet S, et al. A Simple Compressive Sensing Algorithm for Parallel Many-Core Architectures. Technical Report. 2008. http://www-micrel.deis.unibo.it/benini/files/MD/cam08-64.pdf
Liu B, Zou Y M, Ying L. SparseSENSE: application of compressed sensing in parallel MRI. In: Proc of International Conference on Information Technology and Applications in Biomedicine (ITAB), Shenzhen, 2008. 127–130
Chang C H, Ji J. Compressed sensing MRI with multichannel data using multicore processors. Magn Reson Med, 2010, 64: 1135–1139
Tropp J A. Greed is good: Algorithmic results for sparse approximation. IEEE Trans Inf Theory, 2004, 50: 2231–2242
Ben-Haim Z, Eldar Y C, Elad M. Coherence-based performance guarantees for estimating a sparse vector under random noise. IEEE Trans Signal Process, 2010, 58: 5030–5043
Stanley H E. Introduction to Phase Transitions and Critical Phenomena. Oxford: Oxford Press, 1987
Donoho D, Stodden V. Breakdown point of model selection when the number of variables exceeds the number of observations. In: Proc of IEEE International Joint Conference on Neural Network (IJCNN), Vancouver, 2006. 1916–1921
Donoho D L, Tsaig Y. Fast Solution of L1-Norm Minimization Problems When the Solution May Be Sparse. Technical Report. Dept. of Statistics, Stanford University, 2006. http://www-dsp.rice.edu/files/cs/FastL1.pdf
Donoho D, Tanner J. Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing. Philosoph Trans Royal Soc A Math Phys Engin Sci, 2009, 367: 4273–4293
Donoho D, Jin J. Feature selection by higher criticism thresholding achieves the optimal phase diagram. Philosoph Trans Royal Soc A Math Phys Engin Sci, 2009, 367: 4449–4470
Zhu X X, Bamler R. Super-resolution power and robustness of compressive sensing for spectral estimation with application to spaceborne tomographic SAR. IEEE Trans Geosci Remote Sens, 2012, 50: 247–258
Herman M A, Strohmer T. High-resolution radar via compressed sensing. IEEE Trans Signal Process, 2009, 57: 2275–2284
Jiang H. Study on Processing Algorithm and Analysis of Imaging Performance of Compressed Sensing Radar via Information Theory. Master’s thesis. Beijing: Institute of Electronics, Chinese Academy of Sciences, 2011
Jiang C L, Zhang B C, Zhang Z, et al. Experimental results and analysis of Sparse Microwave Imaging from spaceborne radar raw data. Sci China Inf Sci, 2012, 55: 1801–1815
Zhang B C, Hong W, Wu Y R, et al. A Radar Imaging Azimuth Ambiguity Reducing Method Based on ℓ q Regularization (in Chinese). China Patent, 201110310655.5. 2012.
Lin Y G, Zhang B C, Jiang H, et al. Multi-channel SAR imaging based on distributed compressive sensing. Sci China Inf Sci, 2012, 55: 245–259
Lin Y G, Zhang B C, Hong W, et al. Along-track interferometric SAR imaging based on distributed compressed sensing. Electron Lett, 2010, 46: 85–860
Lin Y G. Study on Multi-channel SAR Imaging Based on Compressive Sensing. Ph.D. thesis. Beijing: Institute of Electronics, Chinese Academy of Sciences, 2011
Farhat N H, Werner C L, Chu T H. Prospects for three-dimensional projective and tomographic imaging radar networks. Radio Sci, 1984, 19: 1347–1355
Mahafza B R, Sajjadi M. Three-dimensional SAR imaging using linear array in transverse motion. IEEE Trans Aerosp Electron Syst, 1996, 32: 499–510
Reigber A, Moreira A. First demonstration of airborne SAR tomography using multibaseline L-band data. IEEE Trans Geosci Remote Sens, 2000, 38: 2142–2152
Zhu X X, Bamler R. Tomographic SAR inversion by L1-norm regularization — the compressive sensing approach. IEEE Trans Geosci Remote Sens, 2010, 48: 3839–3846
Baselice F, Ferraioli G, Pascazio V. Three dimensional reconstruction using COSMO-SkyMed high-resolution data. In: Proceedings of Microwaves, Radar and Remote Sensing Symposium (MRRS), 2011. 161–164
Budillon A, Evangelista A, Schirinzi G. Three-dimensional SAR focusing from multipass signals using compressive sampling. IEEE Trans Geosci Remote Sens, 2011, 49: 488–499
Zhu X X, Bamler R. Very High Resolution SAR tomography via compressive sensing. In: Proc of Fringe Workshop Advances in the Science and Applications of SAR Interferometry, Frascati, 2009
Zhu X X, Bamler R. Compressive sensing for high resolution differential SAR tomography-the SL1MMER algorithm. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Honolulu, 2010. 17–20
Zhu X X, Bamler R. Within the resolution cell: Super-resolution in tomographic SAR imaging. In: Proc of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, 2011. 2401–2404
Zhu X X, Bamler R. Demonstration of Super-Resolution for Tomographic SAR Imaging in Urban Environment. IEEE Trans Geosci Remote Sens, 2012, in press
Khomchuk P, Bilik I, Kasilingam D. Compressive sensing-based SAR tomography. In: Proc of IEEE Radar Conference, Washington, 2010. 354–358
Tan W X. Study on Theory and Algorithms for Three-Dimensional Synthetic Aperture Radar Imaging. Ph.D. thesis. Beijing: Institute of Electronics, Chinese Academy of Science, 2009
Wehner D R. High Resolution Radar. Norwood: Artech House Inc, 1987
Zhang L, Xing M, Qiu C W, et al. Achieving higher resolution ISAR imaging with limited pulses via compressed sampling. IEEE Geosci Remote Sens Lett, 2009, 6: 567–71
Wang H, Quan Y, Xing M, et al. ISAR imaging via sparse probing frequencies. IEEE Geosci Remote Sens Lett, 2011, 8: 451–455
Zhang L, Qiao Z, Xing M, et al. High-resolution ISAR imaging with sparse stepped-frequency waveforms. IEEE Trans Geosci Remote Sens, 2011, 49: 4630–4651
Lei Z, Qiao Z, Xing M, et al. High resolution ISAR imaging by exploiting sparse apertures. IEEE Trans Anten Propag, 2011, 60: 997–1008
Zhu F, Zhang Q, Xiang Y, et al. Compressive Sensing in ISAR spectrogram data transmission. In: Proc of Asian-Pacific Conference on Synthetic Aperture Radar (APSAR), Xi’an, 2009. 89–92
Zhu F, Zhang Q, Lei Q, et al. Reconstruction of moving target’s HRRP using sparse frequency-stepped chirp signal. IEEE Sensors J, 2011, 11: 2327–2334
Ye F, Liang D, Zhu J. ISAR enhancement technology based on compressed sensing. Electron Lett, 2011, 47: 620–621
Rao W, Li G, Wang X, et al. ISAR imaging of uniformly rotating targets via parametric weighted L1 minimization. In: Proc of Asian-Pacific Conference on Synthetic Aperture Radar (APSAR), Seoul, 2011
Daniels D J. Surface-penetrating radar. Electron Commun Engin J, 1996, 8: 165–182
Daniels D J. Ground Penetrating Radar. Herts: The Institution of Engineering and Technology, 2004
Gurbuz A C, McClellan J H, Scott W R. Compressive sensing for subsurface imaging using ground penetrating radar. Signal Process, 2009, 89: 1959–1972
Feng X, Sato M. Pre-stack migration applied to GPR for landmine detection. Inverse Problem, 2004, 20: S99
Stolt R H. Migration by Fourier transform. Geophysics, 1978, 43: 23–48
Gurbuz A C, McClellan J H, Scott W R. Compressive sensing for GPR imaging. In: Conference Record of Asilomar Conference on Signals, Systems and Computers (ACSSC), Pacific Grove, 2007. 2223–2227
Gurbuz A C, McClellan J H, Scott W R. GPR imaging using compressed measurements. In: Proc of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Boston, 2008. 11–13
Suksmono A B, Bharata E, Lestari A A, et al. A compressive SFCW-GPR system. In: Proc of 12th Int Conf on Ground Penet Radar, Birmingham, 2008. 16–19
Suksmono A B, Bharata E, Lestari A A, et al. Compressive stepped-frequency continuous-wave ground-penetrating radar. IEEE Geosci Remote Sens Lett, 2010, 7: 665–669
Gurbuz A C, McClellan J H, Scott W R. A compressive sensing data acquisition and imaging method for stepped frequency GPRs. IEEE Trans Signal Process, 2009, 57: 2640–2650
Soldovieri F, Solimene R, Ahmad F. A fast data acquisition and processing scheme for through-the-wall radar imaging. In: Proc SPIE, 2011. 8021
Varshney K R, CII J W, et al. Joint image formation and anisotropy characterization in wide-angle SAR. In: Proc SPIE, 2006. 6237
Stojanovic I, C Proceedings of SPIE, 2008. 6970
Soumekh M. Reconnaissance with slant plane circular SAR imaging. IEEE Trans Image Process, 1996, 5: 1252–1265
Potter L C, Ertin E, Parker J T, et al. Sparsity and compressed sensing in radar imaging. Proc IEEE, 2010, 98: 1006–1020
Lin Y, Hong W, Tan W X, et al. Compressed sensing technique for circular SAR imaging. In: Proc of IET International Radar Conference, Guilin, 2009
Lin Y. Study on Algorithms for Circular Synthetic Aperture Radar Imaging. Ph.D. thesis. Beijing: Institute of Electronics, Chinese Academy of Sciences, 2011
Fishler E, Haimovich A, Blum R, et al. MIMO radar: An idea whose time has come. In: Proc of IEEE Radar Conference, Philadelphia, 2004. 71–78
Li J, Stoica P. MIMO Radar Signal Processing. Hoboken: Wiley, 2009
Bliss D W, Forsythe K W. Multiple-input multiple-output (MIMO) radar and imaging: degrees of freedom and resolution. In: Conference Record of Asilomar Conference on Signals, Systems and Computers (ACSSC), Pacific Grove, 2003. 54–59
Chen C Y, Vaidyanathan P P. Compressed sensing in MIMO radar. In: Conference Record of Asilomar Conference on Signals, Systems and Computers (ACSSC), Pacific Grove, 2008. 41–44
Strohmer T, Friedlander B. Compressed sensing for MIMO radar-algorithms and performance. In: Conference Record of Asilomar Conference on Signals, Systems and Computers (ACSSC), Pacific Grove, 2009. 464–468
Petropulu A P, Yu Y, Poor H V. Distributed MIMO radar using compressive sampling. In: Conference Record of Asilomar Conference on Signals, Systems and Computers (ACSSC), Pacific Grove, 2008. 203–207
Yu Y, Petropulu A, Poor H V. MIMO radar using compressive sampling. IEEE J Sel Top Signal Process, 2010, 4: 146–163
Yu Y, Petropulu A, Poor H. Measurement matrix design for compressive sensing based MIMO radar. IEEE Trans Signal Process, 2011, 59: 5338–5352
Gogineni S, Nehorai A. Target estimation using sparse modeling for distributed MIMO radar. IEEE Trans Signal Process, 2011, 59: 5315–5325
Tan X, Roberts W, Li J, et al. Sparse learning via iterative minimization with application to MIMO radar imaging. IEEE Trans Signal Process, 2011, 59: 1088–1101
Berger C R, Zhou S, Willett P. Signal extraction using compressed sensing for passive radar with OFDM signals. In: Proc of 11th International Conference on Information Fusion, Cologne, 2008
Xu H, He X, Yin Z, et al. Compressive sensing MIMO radar imaging based on inverse scattering model. In: Proc of IEEE International Conference on Signal Processing (ICSP), Beijing, 2010. 1999–2002
Wang J, Li G, Zhang H, et al. SAR Imaging of Moving Targets via Compressive Sensing. ArXiv: 11041074, 2011. http://arxiv.org/pdf/1104.1074
Khwaja A S, Ma J. Applications of compressed sensing for SAR moving-target velocity estimation and image compression. IEEE Trans Instrum Meas, 2011, 60: 2848–2860
Stojanovic I, Karl W C. Imaging of moving targets with multi-static SAR using an overcomplete dictionary. IEEE J Sel Top Signal Process, 2010, 4: 164–176
Ferrara M, Jackson J, Stuff M. Three-dimensional sparse-aperture moving-target imaging. In: Proceedings of SPIE, 2008. 6970
Benz U, Strodl K, Moreira A. A comparison of several algorithms for SAR raw data compression. IEEE Trans Geosci Remote Sens, 1995, 33: 1266–1276
Kwok R, Johnson W. Block adaptive quantization of Magellan SAR data. IEEE Trans Geosci Remote Sens, 1989, 27: 375–383
Bhattacharya S, Blumensath T, Mulgrew B, et al. Fast encoding of synthetic aperture radar raw data using compressed sensing. In: Proc of 14th IEEE Workshop on Statistical Signal Processing, Madison, 2007. 448–452
Bhattacharya S, Blumensath T, Mulgrew B, et al. Synthetic aperture radar raw data encoding using compressed sensing. In: Proc of IEEE Radar Conference, Rome, 2008
Sarvotham S, Baron D, Baraniuk R G. Measurements vs. bits: Compressed sensing meets information theory. In: Proc of 44th Allerton Conf Comm Ctrl Computing, Monticello, 2006
Wainwright M J. Information-theoretic limits on sparsity recovery in the high-dimensional and noisy setting. IEEE Trans Inf Theory, 2009, 55: 5728–5741
Aeron S, Saligrama V, Zhao M. Information theoretic bounds for compressed sensing. IEEE Trans Inf Theory, 2010, 56: 5111–5130
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Zhang, B., Hong, W. & Wu, Y. Sparse microwave imaging: Principles and applications. Sci. China Inf. Sci. 55, 1722–1754 (2012). https://doi.org/10.1007/s11432-012-4633-4
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DOI: https://doi.org/10.1007/s11432-012-4633-4