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An Improving EFA for Clutter Suppression by Using the Persymmetric Covariance Matrix Estimation

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Abstract

The extended factored approach (EFA) is believed to be one of the most efficient and practical space–time adaptive processing (STAP) algorithms for clutter suppression in an airborne radar system. However, it cannot effectively work in the airborne radar system with large antenna array for the huge computational cost and the lack of training sample. To solve these problems, a bi-iterative algorithm based on the persymmetric covariance matrix estimation is proposed in this paper. Firstly, the clutter covariance matrix is estimated by using the original data, the constructed spatial transformed data, the constructed temporal transformed data and the constructed spatial–temporal transformed data. Secondly, the spatial weight vector in EFA is decomposed as the Kronecker products of two short weight vectors. Finally, the bi-iterative algorithm is exploited to obtain the desired weight vectors. Thus, the improving EFA with small training sample demanding is realized. Experimental results demonstrate the effectiveness of the proposed method under small training sample support.

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References

  1. L.E. Brennan, I.S. Reed, Theory of adaptive radar. IEEE Trans. Aerosp. Electron. Syst. 9(2), 237–252 (1973)

    Article  Google Scholar 

  2. S. Chen, L.J. Kong, J.Y. Yang, Adaptive clutter nulling approach for heterogeneous environments. Circuits Syst. Signal Process. 34(3), 987–1000 (2015)

    Article  Google Scholar 

  3. R.C. Dipietro, Extended factored space–time processing for airborne radar systems, in Signals, Systems and Computers, Conference Record of The Twenty-Sixth Asilomar Conference, IEEE (1992), pp. 425–430

  4. R. Fa, R.C. Lamare, L. Wang, Reduced-rank STAP schemes for airborne radar based on switched joint interpolation, decimation and filtering algorithm. IEEE Trans. Signal Process. 58(8), 4182–4194 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  5. D.Z. Feng, W.X. Zheng, A. Cichocki, Matrix-group algorithm via improved whitening process for extracting statistically independent sources from array signals. IEEE Trans. Signal Process. 55(3), 962–977 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  6. J.P. Lasalle, The Stability of Dynamical Systems (SIAM Press, Philadelphia, 1976)

    Book  MATH  Google Scholar 

  7. N. Li, G.L. Cui, L.J. Kong, X.B. Yang, MIMO radar moving target detection against compound-Gaussian clutter. Circuits Syst. Signal Process. 33(6), 1819–1839 (2014)

    Article  Google Scholar 

  8. W.L. Melvin, A STAP overview. IEEE Aerosp. Electron. Syst. Mag. 19(1), 19–35 (2004)

    Article  Google Scholar 

  9. C.D. Peckham, A.M. Haimovich, T.F. Ayoub, J.S. Goldstein, Reduced-rank STAP performance analysis. IEEE Trans. Aerosp. Electron. Syst. 36(2), 664–676 (2000)

    Article  Google Scholar 

  10. I.S. Reed, J.D. Mallett, L.E. Brennan, Rapid convergence rate in adaptive arrays. IEEE Trans. Aerosp. Electron. Syst. 10(6), 853–863 (2007)

    Article  Google Scholar 

  11. P. Stoica, Y. Selen, Cyclic minimizers, majorization techniques, and the expectation-maximization algorithm: a refresher. IEEE Signal Process. Mag. 21(1), 112–114 (2004)

    Article  Google Scholar 

  12. Y.L. Tong, T. Wang, J.X. Wu, Improving EFA-STAP performance using persymmetric covariance matrix estimation. IEEE Trans. Aerosp. Electron. Syst. 51(2), 924–936 (2015)

    Article  Google Scholar 

  13. H. Wang, L. Cai, On adaptive spatial-temporal processing for airborne surveillance radar systems. IEEE Trans. Aerosp. Electron. Syst. 30(3), 660–670 (1994)

    Article  Google Scholar 

  14. C. Xiang, D.Z. Feng, H. Lv, J. He, H.W. Liu, Three-dimensional reduced-dimension transformation for mimo radar space time adaptive processing. Signal Process. 91(8), 2121–2126 (2011)

    Article  MATH  Google Scholar 

  15. Z.C. Yang, Y.L. Qin, R.C. Lamare, H.Q. Wang, X. Li, Sparsity-based direct data domain space–time adaptive processing with intrinsic clutter motion. Circuits Syst. Signal Process. 36(1), 1–28 (2017)

    Article  Google Scholar 

  16. B. Zheng, G.S. Liao, R.B. Wu, Y.H. Zhang, Y.L. Wang, 2-D temporal-spatial adaptive clutter suppression for phased array airborne radars. Acta Electronica Sinica 21(9), 1–7 (1993)

    Google Scholar 

  17. Y. Zhou, D.Z. Feng, G.H. Zhu, W.K. Nie, The post-Doppler adaptive processing method based on the spatial domain reconstruction. Signal Process. 111(c), 89–93 (2015)

    Article  Google Scholar 

  18. Y. Zhou, B. Jiang, W.K. Nie, W.X. Zhang, The space–time processing method based on the spatial decomposition in airborne MIMO radar. Acta Electronica Sinica 45(10), 2348–2354 (2017)

    Google Scholar 

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Acknowledgements

The authors would like to thank very much the Handing Editor and the anonymous reviewers for their valuable comments and suggestions that have significantly improved the manuscript. This work was sponsored in part by National Natural Science Foundation of China under Grants 61503300 and 41601353 and the Scientific Research Plan of Education Department of Shaanxi Province under Grant 17JK0789.

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Authors and Affiliations

  1. School of Information Science and Technology, Northwest University, Xi’an, 710127, China

    Yan Zhou, Lin Wang, Xiaoxuan Chen, Cai Wen, Bo Jiang & Dingyi Fang

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  1. Yan Zhou
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  2. Lin Wang
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  3. Xiaoxuan Chen
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  4. Cai Wen
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  5. Bo Jiang
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  6. Dingyi Fang
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Correspondence to Yan Zhou.

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Zhou, Y., Wang, L., Chen, X. et al. An Improving EFA for Clutter Suppression by Using the Persymmetric Covariance Matrix Estimation. Circuits Syst Signal Process 37, 4136–4149 (2018). https://doi.org/10.1007/s00034-017-0743-y

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  • DOI: https://doi.org/10.1007/s00034-017-0743-y

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