Generating dense and super-resolution ISAR image by combining bandwidth extrapolation and compressive sensing

  • YingHui QuanEmail author
  • Lei Zhang
  • Rui Guo
  • MengDao Xing
  • Zheng Bao
Research Papers


Recent developing compressive sensing (CS) theory indicates that it is possible to obtain precise recovery of a sparse signal from very limited measurements, which provides a new way for data acquisition and signal processing as nature signals usually involve some degree of sparsity. In this paper, we present an algorithm for inversed synthetic aperture radar (ISAR) imaging with super resolution by combining CS and bandwidth extrapolation (BWE) technique. For ISAR imaging, the backscattering field of target is usually contributed by a few strong scattering centers, whose number is much less than that of image pixels. Thus, CS is intuitively suitable for constructing super resolution ISAR image. According to CS theory, the number of extracted dominating scatterers relies on the signal length, which indicates that if only limited data is available, it is difficult to generate dense ISAR image robustly by CS, and some signal components tend to lose. To soften this constraint, BWE is combined with CS imaging to increase the degree of freedom of signal while preserving its coherence. A refined CS-based formation for ISAR image-resolution enhancement is then developed. Both real and simulated data experiments are performed to evaluate the proposed approach, and an example of using this technique demonstrates the enhanced image resolution in application of maneuvering target imaging.


inverse synthetic aperture radar (ISAR) compressive sensing (CS) bandwidth extrapolation (BWE) super resolution 


  1. 1.
    Li J, Stoica P. Efficient mixed-spectrum estimation with applications to target feature extraction. IEEE Trans Signal Proc, 1996, 44: 281–295CrossRefGoogle Scholar
  2. 2.
    Bi Z, Li J, Liu Z S. Super resolution SAR imaging via parametric spectral estimation methods. IEEE Trans Aerosp Electron Syst, 1999, 35: 267–281CrossRefGoogle Scholar
  3. 3.
    Lazarov A D. Iterative minimum mean square error method and recurrent Kalman procedure for ISAR image reconstruction. IEEE Trans Aerosp Electron Syst, 2001, 37: 1432–1441CrossRefGoogle Scholar
  4. 4.
    Liu Z S, Wu R, Li J. Complex ISAR imaging of maneuvering targets via the Capon estimator. IEEE Trans Signal Proc, 1999, 47: 1262–1271CrossRefGoogle Scholar
  5. 5.
    Pdendaal J W, Barnard E, Pistorius CW I. Two-dimensional superresolution radar imaging using the MUSIC algorithm. IEEE Trans Antennas Propag, 1994, 42: 1386–1391CrossRefGoogle Scholar
  6. 6.
    Li J, Stoica P. An adaptive filtering approach to spectral estimation and SAR imaging. IEEE Trans Signal Proc, 1996, 44: 1469–1484CrossRefGoogle Scholar
  7. 7.
    Gupta I J. High-resolution radar imaging using 2-D linear prediction. IEEE Trans Antennas Propag, 1994, 42: 31–37CrossRefGoogle Scholar
  8. 8.
    Moore T G, Zuerndorfer B W, Burt E C. Enhanced imagery using spectral-estimation-based techniques. Lincoln Lab J, 1997, 10: 171–186Google Scholar
  9. 9.
    Li H J, Farhat N H, Shen Y S. A new iterative algorithm for extrapolation of data available in multiple restricted regions with applications to radar imaging. IEEE Trans Antennas Propag, 1987, 35: 581–588CrossRefGoogle Scholar
  10. 10.
    Candès E, Romberg J, Tao T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory, 2006, 52: 489–509CrossRefGoogle Scholar
  11. 11.
    Candès E, Romberg J, Tao T. Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans Inf Theory, 2006, 52: 489–509CrossRefGoogle Scholar
  12. 12.
    Donoho D. Compressed sensing. IEEE Trans Inf Theory, 2006, 52: 5406–5425CrossRefGoogle Scholar
  13. 13.
    Cand’es E J, Wakin M B. An introduction to compressive sampling. IEEE Signal Proc Mag, 2008, 25: 21–30CrossRefGoogle Scholar
  14. 14.
    Zhang L, Xing M D, Qui C W, et al. Achieving higher resolution ISAR imaging with limited pulses via compressed sampling. IEEE Trans Geosci Remote Sens Lett, 2009, 6: 567–571CrossRefGoogle Scholar
  15. 15.
    Ye W, Yeo T S, Bao Z. Weighted least-squares estimation of phase errors for SAR/ISAR autofocus. IEEE Trans Geosci Remote Sens, 1999, 37: 2487–2494CrossRefGoogle Scholar
  16. 16.
    Thayaparan T, Stankovic L, Wernik C, et al. Real-time motion compensation, image formation and image enhancement of moving targets in ISAR and SAR using S-method based approach. IET Signal Proc, 2008, 2: 247–264MathSciNetCrossRefGoogle Scholar
  17. 17.
    Wang Y, Ling H, Chen V C. ISAR motion compensation via adaptive joint time-frequency techniques. IEEE Trans Aerosp Electron Syst, 1998, 34: 670–677CrossRefGoogle Scholar
  18. 18.
    Wang J, Kasilingam D. Global range alignment for ISAR. IEEE Trans Aerosp Electron Syst, 2003, 39: 351–357CrossRefGoogle Scholar
  19. 19.
    Xing M, Wu R, Lan J, et al. High resolution ISAR imaging of high speed moving targets. IEE Proc-Radar Sonar Navig, 2005, 152: 58–67CrossRefGoogle Scholar
  20. 20.
    Candès E, Romberg J, Tao T. Stable signal recovery from incomplete and inaccurate measurements. Comm Pure Appl Math, 2006, 59: 1027–1223CrossRefGoogle Scholar
  21. 21.
    Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory, 2007, 53: 4655–4666MathSciNetCrossRefGoogle Scholar
  22. 22.
    Applebaum L, Howard S, Searle S, et al. Chirp sensing codes: Deterministic compressed sensing measurements for fast recovery. Appl Comput Harmon Anal, 2008, 26: 283–290MathSciNetCrossRefGoogle Scholar
  23. 23.
    Martorella M, Acito N, Berizzi F. Statistical CLEAN technique for ISAR imaging. IEEE Trans Geosci Remote Sens, 2007, 45: 3552–3560CrossRefGoogle Scholar
  24. 24.
    Marple S L. Digital Spectral Analysis with Applications. Englewood Cliffs, NJ: Prentice-Hall, 1987Google Scholar
  25. 25.
    Wu P R. A criterion for radar resolution enhancement with Burg algorithm. IEEE Trans Aerosp Electron Syst, 1995, 31: 877–915CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • YingHui Quan
    • 1
    Email author
  • Lei Zhang
    • 1
  • Rui Guo
    • 1
  • MengDao Xing
    • 1
  • Zheng Bao
    • 1
  1. 1.National Key Lab for Radar Signal ProcessingXidian UniversityXi’anChina

Personalised recommendations