Clutter Mitigation in Airborne Radar Using Deterministic Subspace Clutter and Direct Data Domain

Document Type : Original Article

Authors

Abstract

Space-time adaptive processing is a useful technique for clutter mitigation in airborne radars.
space-time adaptive processing algorithms usually require estimation of interference covariance
matrix with limited training data and high complexity in processing. To overcome these
problems, in this paper, deterministic clutter subspace and the direct data domain method are
introduced. Two methods are presented. In the first proposed method, filter coefficients are
calculated by estimating airborne radar parameters and also calculation of interference
covariance matrix. The sensitivity of estimated parameters is also computed. By extending
clutter subspace, the suggested method could get more robust against parameter estimation. In
the other proposed method, the direct data domain is employed for calculating the coefficients
vector by reducing the size of filters. Finally, the performance of the suggested algorithms is
improved by utilizing other methods such as using more taps and deterministic clutter subspace.

Keywords

References

  1. L. E. Brennan and I. S. Reed, “Theory of adaptive radar,”
  2. IEEE Trans. on aerospace and electronic systems, vol. AES-
  3. , pp. 237-252, March 1973.
  4. J. Ward, “Space-Time adaptive processing for airborne
  5. radar,” MTI Lincoln Laboratory, MTI Technical report 1015,
  6. December 1994.
  7. A. M. Haimovich and Y. Bar-Ness, “An eigenanalysis
  8. interference canceler,” Signal Processing, IEEE Transactions
  9. on, vol. 39, no. 1, pp. 76-84, Jan.1991.
  10. doi: 10.1109/78.80767
  11. A. Haimovich, “The eigencanceler: adaptive radar by
  12. eigenanalysis methods,” Aerospace and Electronic Systems, IEEE Transactions on, vol. 32, no. 2, pp. 532-542, April
  13.  
  14. J. S. Goldstein and I. S. Reed, “Reduced-rank adaptive
  15. filtering,” Signal Processing, IEEE Transactions on , vol. 45,
  16. no. 2, pp. 492-496, Feb. 1997.
  17. M. L. Honig and J. S. Goldstein, “Adaptive reduced-rank
  18. interference suppression based on the multistage Wiener
  19. filter,” Communications, IEEE Transactions on, vol. 50, no.
  20. , pp. 986-994, Jun. 2002.
  21. R. Klemm, “Adaptive clutter suppression for airborne phased
  22. array radars,” IEEE, vol. 130, pp. 125-131, 1983.
  23. W. Xiaoqin, W. Anhong, L. Linsheng, and H. Chongzhao,
  24. “Direct data domain approach to space-time adaptive signal
  25. processing,” Control, Automation, Robotics and Vision
  26. Conference, ICARCV 2004 8th, vol. 3, pp. 2070-2075, 6-9
  27. Dec. 2004.
  28. W. Xiaoqin and H. Chongzhao, “Direct data domain
  29. approach to space-time adaptive processing,” Journal of
  30. Systems Engineering and Electronics, vol. 17, no. 1, pp. 59-
  31. , March 2006.
  32. J. Zhao, Z. Zhaoda, and T. Z. Xuhang, “A modified direct
  33. data domain method in spacetime adaptive processing,”
  34. Robotics, Automation and Mechatronics, 2008 IEEE
  35. Conference on, pp. 1154-1157, 21-24 Sept. 2008.
  36. J. T. Carlo, T. K. Sarkar, and M. C. Wicks, “A least squares
  37. multiple constraint direct data domain approach for STAP,”
  38. Radar Conference, Proceedings of the 2003 IEEE, pp.
  39. -438, 8 May 2003.
Radar
Volume 4, Issue 2 - Serial Number 2
November 2016
Pages 1-10

Files

History

  • Receive Date: 04 November 2014
  • Revise Date: 22 January 2024
  • Accept Date: 19 September 2018
  • Publish Date: 22 July 2016

Share

How to cite

Statistics