logo

SCIENCE CHINA Information Sciences, Volume 55, Issue 8: 1722-1754(2012) https://doi.org/10.1007/s11432-012-4633-4

Sparse microwave imaging: Principles and applications

ZHANG BingChen1,2,1,*,*, HONG Wen1,2,*, WU YiRong1,2,*
More info
  • AcceptedJun 11, 2012
  • PublishedJul 6, 2012

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.


References

[1] Curlander J C, McDonough R N. Synthetic Aperture Radar: Systems and Signal Processing. New York: Wiley, 1991

[2] Henderson F M, Lewis A J. Principles and applications of imaging radar. In: Manual of Remote Sensing. New York:John Wiley and Sons, 1998

[3] Wiley C, Ariz P. Pulsed Doppler Radar Methods and Apparatus. US Patent 3,196,436. 1965

[4] Wiley C A. Synthetic aperture radars: a paradigm for technology evolution. IEEE Trans Aerosp Electron Syst, 1985:21: 440–443

[5] Wikipedia. Synthetic Aperture Radar. https://en.wikipedia.org/wiki/Synthetic aperture radar

[6] NASA. Missions–Seasat 1.

[7] Jordan R L. The Seasat-A synthetic aperture radar system. IEEE J Ocean Eng, 1980, 5: 154–164

[8] CSA. CSA: RadarSat-1.

[9] DLR. TerraSAR-X-Germany’s radar eye in space.

[10] 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

[11] Massonnet D, Souyris J C. Imaging with Synthetic Aperture Radar. Lausanne: EFPL Press, 2008

[12] Brown W M, Porcello L J. An introduction to synthetic-aperture radar. IEEE Spectrum, 1969, 6: 52–62

[13] 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

[14] Moore G E. Cramming more components onto integrated circuits. Electron Mag, 1998, 86: 82–85

[15] Woodward P M. Probability and Information Theory, with Application to Radar. New York: Pergamon, 1953

[16] Cook C E, Bernfeld M. Radar Signals-An Introduction to Theory and Application. Norwood: Artech House, 1993

[17] Nyquist H. Certain topics in telegraph transmission theory. Trans Amer Inst Electr Engin, 1928, 47: 617–644

[18] Shannon C E. Communication in the presence of noise. Proc IRE, 1949, 37: 10–21

[19] Baraniuk R G, Candès E, Elad M, et al. Applications of sparse representation and compressive sensing. Proc IEEE,2010, 98: 906–09

[20] Russell B. History of Western Philosophy. London: George Allen & Unwin Ltd, 1946

[21] Donoho D L. Compressed Sensing. IEEE Trans Inf Theory, 2006. 52: 1289–1306

[22] Candès E J, Tao T. Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans Inf Theory, 2006, 52: 5406–5425

[23] Candès E J, Romberg J K, Tao T. Stable signal recovery from incomplete and inaccurate measurements. Commun Pure Appl Math, 2006, 59: 1207–1223

[24] Mallat S, Yu G. Super-resolution with sparse mixing estimators. IEEE Trans Image Process, 2010, 19: 2889–2900

[25] 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

[26] 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

[27] Baraniuk R, Steeghs P. Compressive radar imaging. In: Proc of IEEE Radar Conference, Boston, 2007. 128–133

[28] Patel V M, Easley G R, Healy D, et al. Compressed synthetic aperture radar. IEEE J Sel Top Signal Process, 2010,4: 244–254

[29] Ender J H G. On compressive sensing applied to radar. Signal Process, 2010, 90: 1402–1414

[30] Wu Y R. Studies on theory, system, and methodology of Sparse Microwave Imaging. Statement Tasks and Project Plan of 973 Program: 2009

[31] Cumming I G, Wong F H. Digital Signal Processing of Synthetic Aperture Radar Data: Algorithms and Implementation. Norwood: Artech House, 2004

[32] Raney R K, Runge H, Bamler R, et al. Precision SAR processing using chirp scaling. IEEE Trans Geosci Remote Sens, 1994, 32: 786–799

[33] Bamler R. A comparison of range-Doppler and wavenumber domain SAR focusing algorithms. IEEE Trans Geosci Remote Sens, 1992, 30: 706–713

[34] Basu S, Bresler Y. O(N2 log2 N) filtered backprojection reconstruction algorithm for tomography. IEEE Trans Image Process, 2000, 9: 1760–1773

[35] Xiao S, Munson Jr D C, Basu S, et al. An N2 log2 N back-projection algorithm for SAR image formation. In: Conference Record of Asilomar Conference on Signals, Systems and Computers (ACSSC), Pacific Grove, 2000

[36] Suess M, Grafm¨uller 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

[37] Suess M. Side-Looking Synthetic Aperture Radar System. European Patent 1,241,487. 2006

[38] Currie A, Brown M A. Wide-swath SAR. IEE Proc F Radar Signal Process, 1992, 139: 122–135

[39] Elad M. Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. New York: Springer, 2010

[40] Mallat S G, Zhang Z. Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process, 1993, 41:3397–3415

[41] Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit. SIAM J Sci Comput, 1999, 20: 33–61

[42] Candès E J, Tao T. Decoding by linear programming. IEEE Trans Inf Theory, 2005, 51: 4203–4215

[43] Candès E, Tao T. The Dantzig selector: Statistical estimation when p is much larger than n. The Annal Stat, 2007,35: 2313–2351

[44] 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

[45] Candès E, Romberg J. Sparsity and incoherence in compressive sampling. Inverse Problem, 2007, 23: 969–985

[46] Tsaig Y, Donoho D L. Extensions of compressed sensing. Signal Process, 2006, 86: 549–571

[47] Baraniuk R G. More is less: Signal processing and the data deluge. Science, 2011, 331: 717–719

[48] Baron D, Wakin M B, Duarte M F, et al. Distributed Compressed Sensing. Technical Report. Rice: Rice University,2005,

[49] 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

[50] 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

[51] Ahmed N, Natarajan T, Rao K R. Discrete cosine transform. IEEE Trans Comput, 1974, 100: 90–93

[52] Daubechies I. Ten Lectures on Wavelets. Philadelphia: SIAM Publications, 2006

[53] Ron A, Shen Z. Affine systems in L2(d): the analysis of the Analysis operator. J Function Analys, 1997,148: 408–447

[54] Velisavljevic V, Dragotti P L, Vetterli M. Directional wavelet transforms and frames. In: Proc of Int Conf Image Processing, Rochester, 2002. 589–592

[55] Candès E J. Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edges. Technical Report. DTIC Document, 2000. www.curvelet.org/papers/Curve99.pdf

[56] 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

[57] Oliver C, Quegan S. Understanding Synthetic Aperture Radar Images. Raleigh: SciTech Publishing, 2004

[58] 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

[59] 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

[60] 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

[61] 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

Copyright 2019 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1