Skip to content

Advertisement

Open Access

Fast Iterative Subspace Algorithms for Airborne STAP Radar

EURASIP Journal on Advances in Signal Processing20062006:037296

https://doi.org/10.1155/ASP/2006/37296

Received: 16 December 2005

Accepted: 16 July 2006

Published: 10 September 2006

Abstract

Space-time adaptive processing (STAP) is a crucial technique for the new generation airborne radar for Doppler spread compensation caused by the platform motion. We here propose to apply range cell snapshots-based recursive algorithms in order to reduce the computational complexity of the conventional STAP algorithms and to deal with a possible nonhomogeneity of the data samples. Subspace tracking algorithms as PAST, PASTd, OPAST, and more recently the fast approximate power iteration (FAPI) algorithm, which are time-based recursive algorithms initially introduced in spectral analysis, array processing, are good candidates. In this paper, we more precisely investigate the performance of FAPI for interference suppression in STAP radar. Extensive simulations demonstrate the outperformance of FAPI algorithm over other subspace trackers of similar computational complexity. We demonstrate also its effectiveness using measured data from the multichannel radar measurements (MCARM) program.

Keywords

RadarRecursive AlgorithmRadar MeasurementRange CellPlatform Motion

[12345678910111213141516]

Authors’ Affiliations

(1)
Laboratoire des Signaux et Systèmes (LSS), CNRS, Supélec, Gif-sur-Yvette Cedex, France

References

  1. Klemm R: Space-Time Adaptive Processing: Principles and Applications, IEE Radar, Sonar, Navigation and Avionics. Volume 9. IEE Press, London, UK; 2000.Google Scholar
  2. Skolnik MI: Radar Handbook. McGraw-Hill, New York, NY, USA; 1990.Google Scholar
  3. Brennan LE, Reed LS: Theory of adaptive radar. IEEE Transactions on Aerospace and Electronic Systems 1973, 9(2):237-252.View ArticleGoogle Scholar
  4. Haimovich A: Eigencanceler: adaptive radar by eigenanalysis methods. IEEE Transactions on Aerospace and Electronic Systems 1996, 32(2):532-542.View ArticleGoogle Scholar
  5. Hua Y, Xiang Y, Chen T, Abed-Meraim K, Miao Y: A new look at the power method for fast subspace tracking. Digital Signal Processing 1999, 9(4):297-314. 10.1006/dspr.1999.0348View ArticleGoogle Scholar
  6. Badeau R, David B, Richard G: Fast approximated power iteration subspace tracking. IEEE Transactions on Signal Processing 2005, 53(8):2931-2941.MathSciNetView ArticleGoogle Scholar
  7. Yang B: Projection approximation subspace tracking. IEEE Transactions on Signal Processing 1995, 43(1):95-107. 10.1109/78.365290View ArticleGoogle Scholar
  8. Abed-Meraim K, Chkeif A, Hua Y: Fast orthonormal PAST algorithm. IEEE Signal Processing Letters 2000, 7(3):60-62. 10.1109/97.823526View ArticleGoogle Scholar
  9. Himed B: MCARM/STAP data analysis. In Tech. Rep. AFRL-SN-RS-TR-1999-48. Air Force Research Laboratory, Dayton, Ohio, USA; May 1999. Vol IIGoogle Scholar
  10. Ward J: Space-time adaptive processing for airborne radar. In Tech. Rep. 1015. MIT Lincoln Laboratory, Lexington, Mass, USA; December 1994.Google Scholar
  11. Richardson PG: STAP covariance matrix structure and its impact on clutter plus jamming suppression solutions. Electronics Letters 2001, 37(2):118-119. 10.1049/el:20010090View ArticleGoogle Scholar
  12. Hua Y: Asymptotical orthonormalization of subspace matrices without square root. IEEE Signal Processing Magazine 2004, 21(4):56-61. 10.1109/MSP.2004.1311143View ArticleGoogle Scholar
  13. Jung-Lang Y: A novel subspace tracking using correlation-based projection approximation. Signal Processing 2000, 80(12):2517-2525. 10.1016/S0165-1684(00)00138-9View ArticleMATHGoogle Scholar
  14. Kamenetsky M, Widrow B: A variable leaky LMS adaptive algorithm. Proceedings of the IEEE conference of the 38th Asilomar on Signals, Systems and Computers, November 2004, Pacific Grove, Calif, USA 1: 125-128.Google Scholar
  15. Kim YL, Pillai SU, Guerci JR: Optimal loading factor for minimal sample support space-time adaptive radar. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '98), May 1998, Seattler, Wash, USA 4: 2505-2508.Google Scholar
  16. Goldstein JS, Reed IS, Zulch PA: Multistage partially adaptive stap cfar detection algorithm. IEEE Transaction on Aerospace and Electronic Systems 1999, 35(2):645-662. 10.1109/7.766945View ArticleGoogle Scholar

Copyright

© H. Belkacemi and S. Marcos 2006

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement