Sparse Bayesian learning in ISAR tomography imaging

详细信息    查看全文

• 作者：Wu-ge Su ; Hong-qiang Wang ; Bin Deng …
• 关键词：inverse synthetic aperture radar (ISAR) ; tomography ; computer aided tomography (CT) imaging ; sparse recover ; compress sensing (CS) ; sparse Bayesian learning (SBL)
• 刊名：Journal of Central South University of Technology
• 出版年：2015
• 出版时间：May 2015
• 年：2015
• 卷：22
• 期：5
• 页码：1790-1800
• 全文大小：2,121 KB
• 参考文献：[1]CHEN V C, QIAN S. Joint time-frequency transform for radar range-Doppler imaging [J]. IEEE Trans on Aerosp Electron Syst, 1998, 34(2): 486-99.View Article MathSciNet
  [2]RANEY R K, RUNGE H, BAMLER R. Precision SAR processing using chirp scaling [J]. IEEE Trans on GRS, 1994, 32(7): 786-99.
  [3]HUANG Pei-kang, YIN Hong-cheng, XU Xiao-jian. Radar target characteristics [M]. Beijing: Publishing House of Electronic Industry, 2005: 266-69. (in Chinese).
  [4]DAVID C M, JAMES D O, WKENNETH J. A tomographic formulation of spotlight-mode synthetic aperture radar [J]. Proc IEEE, 1983, 71(9): 917-25.
  [5]KAK A C, SLANEY M. Principles of computerized tomographic imaging [M]. New York: IEEE Press, 1999.
  [6]CHEN Lei, SHAN Ou-yang. A time-domain beamformer for UWB through wall imaging [C]// Proceeding IEEE Region 10 Conference. Taipei: IEEE Press, 2007: 1-.
  [7]JAKOWATZ C V, WAHL D E, PAUL H D, THOMPSON P A. Spotlight-mode synthetic aperture radar: A signal processing approach [M]. Boston: Kluwer Academic Publishers, 1996.View Article
  [8]DESAI M D, JENKINS W K. Convolution back projection image reconstruction for spotlight mode synthetic aperture radar [J]. IEEE Trans on Image Process, 1992, 1(4): 505-17.View Article
  [9]GLENTIS G O, ZHAO Ke-xin, JAKOBSSON A, LI Jian. Non-parametric high-resolution SAR imaging [J]. IEEE Trans on Signal Processing, 2013, 61(7): 1614-624.View Article MathSciNet
  [10]VU D, XUE Ming, TAN Xing, LI Jian. A Bayesian approach to SAR imaging [J]. Digital Signal Processing, 2013, 23(3): 852-58.View Article MathSciNet
  [11]GERRY M J, POTTER L C, GUPTA I J, MERWE A V D. A parametric model for synthetic aperture radar measurements [J]. IEEE Transactions on Antennas and Propagation, 1999, 47(7): 1179-188.View Article
  [12]ZOU Fei, LI Xiang, ROBERTO T. Inverse synthetic aperture radar imaging based on sparse signal processing [J]. Journal of Central South University of Technology, 2011, 18(5): 1609-613.View Article
  [13]DU Xiao-yong, DUAN Chong-wen, HU Wei-dong. Sparse representation based autofocusing technique for ISAR images [J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3): 1826-835.View Article
  [14]CANDES E, ROMBERG J, TAO T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information [J]. IEEE Transactions on Information Theory, 2006, 52(2): 489-09.View Article MATH MathSciNet
  [15]ENDER J H G. On compressive sensing applied to radar [J]. Signal Processing, 2010, 90(5): 1402-414.View Article MATH
  [16]CETIN M, KARL W C. Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization [J]. IEEE Trans on Image Process, 2001, 10(4): 623-31.View Article MATH
  [17]TROPP J A, WRIGHT J. Computational methods for sparse solution of linear inverse problems [J]. Proc IEEE, 2010, 98(6): 948-58.View Article
  [18]SADEGH S, ETIN M, ALI M, SHIRAZI M. Multiple feature-enhanced SAR imaging using sparsity in combined dictionaries [J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(4): 821-25View Article
  [19]NEEDELL D, TROPP J A. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples [J]. Appl and Comp Harmonic Anal, 2009, 26(3): 301-21.View Article MATH MathSciNet
  [20]NEEDELL D, VERSHYNIN R. Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit [J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2): 310-16.View Article
  [21]TIPPING M E. Sparse Bayesian learning and the relevance vector machine [J]. Journal of Machine Learning Research, 2001, 2(1): 211-44.MathSciNet
  [22]WIPF D P, RAO B D. Sparse Bayesian learning for basis selection [J]. IEEE Trans on Signal Processing, 2004, 52(8): 2153-164.View Article MathSciNet
  [23]JI Shi-hao, XUE Ya, CARIN L. Bayesian compressive sensing [J]. IEEE Trans on Signal Processing, 2008, 56(6): 2346-356.View Article MathSciNet
  [24]BABACAN S D, MOLINA R, KATSAGGELOS A K. Bayesian compressive sensing using Laplace priors [J]. IEEE Trans on Signal Processing, 2010, 19(1): 53-3.MathSciNet
  [25]SU Wu-ge, WANG Hong-qiang, YANG Zhao-cheng. SAR imaging based on attributed scatter model using sparse recovery techniques [J]. Journal of Central South University, 2014, 21(1): 223-31.View Article
  [26]ZHANG Zhi-ling, RAO B. Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning [J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(5): 912-26.View Article
  [27]ZHANG Zhi-ling, RAO B. Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation [J]. IEEE Trans on Signal Processing, 2013, 61(8): 2009-015.View Article
  [28]HOSEIN G M, ZADEH M B, JUTTEN C. A fast approach for over complete sparse decomposition based on smoothed l0-norm [J]. IEEE Trans on Signal Process, 2009, 57(1): 289-01.View Article
  [29]YARDIBI T, LI
• 作者单位：Wu-ge Su (1)
  Hong-qiang Wang (1)
  Bin Deng (1)
  Rui-jun Wang (1)
  Yu-liang Qin (1)
  
  1. School of Electronic Science and Engineering, National University of Defense Technology, Changsha, 410073, China
  
• 刊物类别：Engineering
• 刊物主题：Engineering, general
  Metallic Materials
  Chinese Library of Science
  
• 出版者：Central South University, co-published with Springer
• ISSN：2227-5223

文摘

Inverse synthetic aperture radar (ISAR) imaging can be regarded as a narrow-band version of the computer aided tomography (CT). The traditional CT imaging algorithms for ISAR, including the polar format algorithm (PFA) and the convolution back projection algorithm (CBP), usually suffer from the problem of the high sidelobe and the low resolution. The ISAR tomography image reconstruction within a sparse Bayesian framework is concerned. Firstly, the sparse ISAR tomography imaging model is established in light of the CT imaging theory. Then, by using the compressed sensing (CS) principle, a high resolution ISAR image can be achieved with limited number of pulses. Since the performance of existing CS-based ISAR imaging algorithms is sensitive to the user parameter, this makes the existing algorithms inconvenient to be used in practice. It is well known that the Bayesian formalism of recover algorithm named sparse Bayesian learning (SBL) acts as an effective tool in regression and classification, which uses an efficient expectation maximization procedure to estimate the necessary parameters, and retains a preferable property of the l ₀-norm diversity measure. Motivated by that, a fully automated ISAR tomography imaging algorithm based on SBL is proposed. Experimental results based on simulated and electromagnetic (EM) data illustrate the effectiveness and the superiority of the proposed algorithm over the existing algorithms.