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Improved eigenanalysis canceler based on data-independent clutter subspace estimation for space-time adaptive processing

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Abstract

The eigenanalysis canceler (EC) method which is suitable for nonhomogeneous clutter can suppress clutter effectively by discarding the eigenvectors of small eigenvalues, which is a well-known subspace-based space-time adaptive processing (STAP) method. However, the computational complexity of conventional EC STAP method is huge due to the eigenvalue decomposition. Moreover, the corresponding performance would be degraded significantly by the subspace leakage phenomenon, since the clutter subspace is not strictly confined to a low-rank subspace any more. Therefore, an improved EC STAP method based on the data-independent clutter subspace estimation is proposed to reduce the computational complexity, where the clutter subspace is rapidly constructed by sampling the prolate spheroidal wave functions (PSWF) non-uniformly. In order to deal with the subspace leakage phenomenon, the proposed EC-PSWF STAP method is modified based on the covariance matrix taper (CMT) to obtain the covariance matrix by re-establishing the noise floor. The corresponding performance of proposed method is evaluated by using the numerical simulation.

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References

  1. Ward J. Space-time adaptive processing for airborne radar. Technical Report 1015, Lincoln Laboratory, MIT. 1994

    Google Scholar 

  2. Guerci J R. Space-Time Adaptive Processing for Radar. Artech House, 2003

    Google Scholar 

  3. Yang X P, Liu Y X, Long T. Pulse-order recursive method for inverse covariance matrix compution applied to spacetime adaptive processing. Sci China Inf Sci, 2013, 56: 042312

    MathSciNet  Google Scholar 

  4. Wu R B, Jia Q Q, Li H. A novel STAP method for the detection of fast air moving target from high speed platform. Sci China Inf Sci, 2012, 55: 1259–1269

    Article  MathSciNet  Google Scholar 

  5. Gong W F, Sun X. An improved reduced-rand STAP interference suppression method in design of GNSS receivers. Sci China Inf Sci, 2012, 55: 2329–2342

    Article  MathSciNet  MATH  Google Scholar 

  6. Reed I S, Mallett J D, Brennan L E. Rapid convergence rate in adaptive arrays. IEEE Trans Aerosp Electron Syst, 1974, 10: 853–863

    Article  Google Scholar 

  7. Fa R, de Lamare R C. Reduced-rank STAP algorithms using joint iterative optimization of filters. IEEE Trans Aerosp Electron Syst, 2011, 47: 1668–1684

    Article  Google Scholar 

  8. Gierull C H, Balaji B. Minimal sample support space-time adaptive processing with fast subspace techniques. IEE Proc-Radar Sonar Navig, 2002, 149: 209–220

    Article  Google Scholar 

  9. Haimovich A, Bar-Ness Y. An eigenanalysis interference canceler. IEEE Trans Signal Process, 1991, 39: 76–84

    Article  Google Scholar 

  10. Haimovich A. The eigencanceler: adaptive radar by eigenanalysis methods. IEEE Trans Aerosp Electron Syst, 1996, 32: 532–542

    Article  Google Scholar 

  11. Goldstein J S, Reed I S. Reduced rank adaptive filtering. IEEE Trans Signal Process, 1997, 45: 492–496

    Article  Google Scholar 

  12. Goldstein J S, Reed I S, Zulch P A. Multistage partially adaptive STAP CFAR detection algorithm. IEEE Trans Aerosp Electron Syst, 1999, 35: 645–661

    Article  Google Scholar 

  13. Guerci J R, Goldstein J R, Reed I S. Optimal and adaptive reduced-rank STAP. IEEE Trans Aerosp Electron Syst, 2000, 36: 647–663

    Article  Google Scholar 

  14. Chen C Y, Vaidyanathan P P. MIMO radar space-time adaptive processing using prolate spheroidal wave function. IEEE Trans Signal Process, 2008, 56: 623–635

    Article  MathSciNet  Google Scholar 

  15. Yang X P, Liu Y X, Hu X N, et al. Robust generalized inner products algorithm using prolate spheroidal wave functions. In: IEEE Radar Conference, Atlanta, 2012. 581–584

    Google Scholar 

  16. Guerci J R, Bergin J S. Principal components, covariance matrix tapers, and the subspace leakage problem. IEEE Trans Aerosp Electron Syst, 2002, 38: 152–162

    Article  Google Scholar 

  17. Guerci J R. Theory and application of covariance matrix tapers for robust adaptive beamforming. IEEE Trans Signal Process, 1999, 47: 977–985

    Article  Google Scholar 

  18. Melvin W L, Showman G A. An approach to knowledge-aided covariance estimation. IEEE Trans Aerosp Electron Syst, 2006, 42: 1021–1042

    Article  Google Scholar 

  19. Carlson B D. Covariance matrix estimation errors and diagonal loading in adaptive arrays. IEEE Trans Aerosp Electron Syst, 1988, 24: 397–401

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China

    Teng Long, YongXu Liu, XiaoPeng Yang & YuZe Sun

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  1. Teng Long
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  2. YongXu Liu
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  3. XiaoPeng Yang
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  4. YuZe Sun
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Correspondence to XiaoPeng Yang.

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Long, T., Liu, Y., Yang, X. et al. Improved eigenanalysis canceler based on data-independent clutter subspace estimation for space-time adaptive processing. Sci. China Inf. Sci. 56, 1–10 (2013). https://doi.org/10.1007/s11432-013-5003-6

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  • DOI: https://doi.org/10.1007/s11432-013-5003-6

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