Volume 5 Issue 1
Feb.  2016
Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2016  >  5(1): 1-7
Li Gang, Xia Xiang-Gen. Parametric Sparse Representation and Its Applications to Radar Sensing[J]. Journal of Radars, 2016, 5(1): 1-7. doi: 10.12000/JR15126
Citation: Li Gang, Xia Xiang-Gen. Parametric Sparse Representation and Its Applications to Radar Sensing[J]. Journal of Radars, 2016, 5(1): 1-7. doi: 10.12000/JR15126
shu

Parametric Sparse Representation and Its Applications to Radar Sensing

DOI: 10.12000/JR15126
  • 1.

    (Department of Electronic Engineering, Tsinghua University, Beijing 100084, China)

  • 2.

    (Department of Electrical & Computer Engineering, University of Delaware, Newark, DE 19716, USA)

Funds:

The National Natural Science Foundation of China (61422110, 41271011), The National Ten Thousand Talent Program-Young Top-Notch Talent Program, The Tsinghua University Initiative Scientific Research Program, The Tsinghua National Laboratory for Information Science and Technology (TNList) Program

  • Received Date: 2015-12-14
  • Rev Recd Date: 2016-01-06
  • Publish Date: 2016-02-28
    Abstract
  • Sparse signal processing has been utilized to the area of radar sensing. Due to the presence of unknown factors such as the motion of the targets of interest and the error of the radar trajectory, a predesigned dictionary cannot provide the optimally spare representation of the actual radar signals. This paper will introduce a method called parametric sparse representation, which is a special case of dictionary learning and can dynamically learn the unknown factors during the radar sensing and achieve the optimally sparse representation of radar signals. This paper will also introduce the applications of parametric sparse representation to Inverse Synthetic Aperture Radar imaging (ISAR) imaging, Synthetic Aperture Radar imaging (SAR) autofocusing and target recognition based on micro-Doppler effect.

     

    • FullText(HTML)
  • loading
    • References(31)
  • [1]
    Tibshirani R. Regression shrinkage and selection via the lasso[J]. Journal of the Royal Statistical Society, Series B (Methodological), 1996: 267-288.
    [2]
    Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
    [3]
    Cands E J, Romberg J, and Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.
    [4]
    Candes E J and Tao T. Near-optimal signal recovery from random projections: universal encoding strategies[J]. IEEE Transactions on Information Theory, 2006, 52(12): 5406-5425.
    [5]
    Eldar Y and Kutyniok G. Compressed Sensing: Theory and Applications[M]. Cambridge University Press, 2012.
    [6]
    Baraniuk R and Steeghs P. Compressive radar imaging[C]. 2007 IEEE Radar Conference, 2007: 128-133.
    [7]
    Potter L C, Ertin E, Parker J T, et al.. Sparsity and compressed sensing in radar imaging[J]. Proceedings of the IEEE, 2010, 98(6): 1006-1020.
    [8]
    Ender J H G. On compressive sensing applied to radar[J]. Signal Processing, 2010, 90(5): 1402-1414.
    [9]
    Zhang B, Hong W, and Wu Y. Sparse microwave imaging: principles and applications[J]. SCIENCE CHINA Information Sciences, 2012, 55(8): 1722-1754.
    [10]
    吴一戎, 洪文, 张冰尘, 等. 稀疏微波成像研究进展[J]. 雷达学报, 2014, 3(4): 383-395. Wu Yi-rong, Hong Wen, Zhang Bing-chen, et al.. Current developments of sparse microwave imaging[J]. Journal of Radars, 2014, 3(4): 383-395.
    [11]
    Hong W, Zhang B, Zhang Z, et al.. Radar imaging with sparse constraint: principle and initial experiment[C]. Proceedings of 10th European Conference on Synthetic Aperture Radar, EUSAR 2014, Berlin, Germany, 2014: 1-4.
    [12]
    Tropp J A and Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666.
    [13]
    Dai W and Milenkovic O. Subspace pursuit for compressive sensing signal reconstruction[J]. IEEE Transactions on Information Theory, 2009, 55(5): 2230-2249.
    [14]
    Needell D and Tropp J A. CoSaMP: iterative signal recovery from incomplete and inaccurate samples[J]. Applied and Computational Harmonic Analysis, 2009, 26(3): 301-321.
    [15]
    Li G, Zhang H, Wang X, et al.. ISAR 2-D imaging of uniformly rotating targets via matching pursuit[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(2): 1838-1846.
    [16]
    Rao W, Li G, Wang X, et al.. Adaptive sparse recovery by parametric weighted L1 minimization for ISAR imaging of uniformly rotating targets[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2): 942-952.
    [17]
    Rao W, Li G, Wang X, et al.. Comparison of parametric sparse recovery methods for ISAR image formation[J]. SCIENCE CHINA Information Sciences, 2014, 57(2). doi: 10.1007/s11432-013-4859-9.
    [18]
    Rao W, Li G, Wang X, et al.. Parametric sparse representation method for ISAR imaging of rotating targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 910-919.
    [19]
    Chen Y, Li G, Zhang Q, et al.. Parametric sparse representation method for air-borne SAR autofocusing. submitted to IEEE Transactions on Geoscience and Remote Sensing.
    [20]
    Li G and Varshney P K. Micro-Doppler parameter estimation via parametric sparse representation and pruned orthogonal matching pursuit[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(12): 4937-4948.
    [21]
    Gaglione D, Clemente C, Coutts F, et al.. Model-based sparse recovery method for automatic classification of helicopters[C]. 2015 IEEE Radar Conference, Arlington, 2015: 1161-1165.
    [22]
    Alonso M T, Lopez-Dekker P, and Mallorqui J J. A novel strategy for radar imaging based on compressive sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(12): 4285-4295.
    [23]
    Coutts F K, Gaglione D, Clemente C, et al.. Label consistent K-SVD for sparse micro-Doppler classification[C]. 2015 IEEE International Conference on Digital Signal Processing, Singapore, 2015: 90-94.
    [24]
    Peyre G. Best basis compressed sensing[J]. IEEE Transactions on Signal Processing, 2010, 58(5): 2613-2622.
    [25]
    Zhu H, Leus G, and Giannakis G B. Sparsity-cognizant total least-squares for perturbed compressive sampling[J]. IEEE Transactions on Signal Processing, 2011, 59(5): 2002-2016.
    [26]
    Olshausen B A and Field D J. Sparse coding with an overcomplete basis set: a strategy employed by V1?[J]. Vision Research, 1997, 37(23): 3311-3325.
    [27]
    Kreutz-Delgado K, Murray J F, Rao B D, et al.. Dictionary learning algorithms for sparse representation[J]. Neural Computation, 2003, 15(2): 349-396.
    [28]
    Bryta O and Elad M. Compression of facial images using the K-SVD algorithm[J]. Journal of Visual Communication and Image Representation, 2008, 19(4): 270-282.
    [29]
    Tosic I and Frossard P. Dictionary learning[J]. IEEE Signal Processing Magazine, 2011, 28(2): 27-38.
    [30]
    Donohoe G W. Subaperture autofocus for synthetic aperture radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(2): 617-621.
    [31]
    Wahl D E, Eichel P H, Ghiglia D C, et al.. Phase gradient autofocusa robust tool for high resolution SAR phase correction[J]. IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(3): 827-835.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(2989) PDF downloads(3059) Cited by()
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return