Skip to main content

Eigenspace-Based Motion Compensation for ISAR Target Imaging

Abstract

A novel motion compensation technique is presented for the purpose of forming focused ISAR images which exhibits the robustness of parametric methods but overcomes their convergence difficulties. Like the most commonly used parametric autofocus techniques in ISAR imaging (the image contrast maximization and entropy minimization methods) this is achieved by estimating a target's radial motion in order to correct for target scatterer range cell migration and phase error. Parametric methods generally suffer a major drawback, namely that their optimization algorithms often fail to converge to the optimal solution. This difficulty is overcome in the proposed method by employing a sequential approach to the optimization, estimating the radial motion of the target by means of a range profile cross-correlation, followed by a subspace-based technique involving singular value decomposition (SVD). This two-stage approach greatly simplifies the optimization process by allowing numerical searches to be implemented in solution spaces of reduced dimension.

References

  1. 1.

    Chen C-C, Andrews HC: Target-motion-induced radar imaging. IEEE Transactions on Aerospace and Electronic Systems 1980, 16(1):2–14.

    Article  Google Scholar 

  2. 2.

    Wang G, Bao Z: The minimum entropy criterion of range alignment in ISAR motion compensation. Proceedings of the IEEE International Radar Conference, October 1997, Edinburgh, UK 236–239.

    Google Scholar 

  3. 3.

    Steinberg BD: Microwave imaging of aircraft. Proceedings of the IEEE 1988, 76(12):1578–1592. 10.1109/5.16351

    Article  Google Scholar 

  4. 4.

    Carrara WC, Goodman RS, Majewski RM: Spotlight Synthetic Aperture Radar: Signal Processing Algorithms. Artech House, Boston, Mass, USA; 1995.

    Google Scholar 

  5. 5.

    Haywood B, Evans RJ: Motion compensation for ISAR imaging. Proceedings of Australian Symposium on Signal Processing and Applications (ASSPA '89), April 1989, Adelaide, Australia 113–117.

    Google Scholar 

  6. 6.

    Wu H, Grenier D, Delisle GY, Fang D-G: Translational motion compensation in ISAR image processing. IEEE Transactions on Image Processing 1995, 4(11):1561–1571. 10.1109/83.469937

    Article  Google Scholar 

  7. 7.

    Attia E: Self-cohering airborne distributed arrays on land clutter using the robust minimum variance algorithm. Proceedings of IEEE Antennas and Propagation Society International Symposium (APS '86), June 1986 24: 603–606.

    Google Scholar 

  8. 8.

    Wahl DE, Eichel PH, Ghiglia DC, Jakowatz CV Jr.: Phase gradient autofocus—a robust tool for high resolution SAR phase correction. IEEE Transactions on Aerospace and Electronic Systems 1994, 30(3):827–835. 10.1109/7.303752

    Article  Google Scholar 

  9. 9.

    Berizzi F, Corsini G: Autofocusing of inverse synthetic aperture radar images using contrast optimization. IEEE Transactions on Aerospace and Electronic Systems 1996, 32(3):1185–1191.

    Article  Google Scholar 

  10. 10.

    Xi L, Guosui L, Ni J: Autofocusing of ISAR images based on entropy minimization. IEEE Transactions on Aerospace and Electronic Systems 1999, 35(4):1240–1252. 10.1109/7.805442

    Article  Google Scholar 

  11. 11.

    Wu H, Grenier D, Delisle GY, Fang D-G: Translational motion compensation in ISAR image processing. IEEE Transactions on Image Processing 1995, 4(11):1561–1571. 10.1109/83.469937

    Article  Google Scholar 

  12. 12.

    Wang Y, Ling H, Chen VC: ISAR motion compensation via adaptive joint time-frequency technique. IEEE Transactions on Aerospace and Electronic Systems 1998, 34(2):670–677. 10.1109/7.670350

    Article  Google Scholar 

  13. 13.

    Berizzi F, Dalle Mese E, Martorella M: Performance analysis of a contrast-based ISAR autofocusing algorithm. Proceedings of the IEEE International Radar Conference, April 2002, Long Beach, Calif, USA 200–205.

    Google Scholar 

  14. 14.

    Li J, Wu R, Chen VC: Robust autofocus algorithm for ISAR imaging of moving targets. IEEE Transactions on Aerospace and Electronic Systems 2001, 37(3):1056–1069. 10.1109/7.953256

    Article  Google Scholar 

  15. 15.

    Berizzi F, Martorella M, Haywood B, Dalle Mese E, Bruscoli S: A survey on ISAR autofocusing techniques. Proceedings of IEEE International Conference on Image Processing (ICIP '04), October 2004, Singapore 1: 9–12.

    Google Scholar 

  16. 16.

    Ausherman DA, Kozmer A, Walker JL, Jones HM, Poggio EC: Developments in radar imaging. IEEE Transactions on Aerospace and Electronic Systems 1984, 20(4):363–400.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to D. Yau.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Yau, D., Berry, P.E. & Haywood, B. Eigenspace-Based Motion Compensation for ISAR Target Imaging. EURASIP J. Adv. Signal Process. 2006, 090716 (2006). https://doi.org/10.1155/ASP/2006/90716

Download citation

Keywords

  • Singular Value Decomposition
  • Parametric Method
  • Motion Compensation
  • Radial Motion
  • Range Cell