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Adaptive Detection of Distributed Targets in Gaussian Clutter Based on Multiple A-Priori Spectral Models

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Abstract

In this paper, we consider the problem of adaptive detection for distributed targets embedded in Gaussian disturbance without secondary data. We suppose that some a-priori spectral models for the interference in the cells under test and a lower bound on the power spectral density of the white disturbance term are available. First, we propose an approximate estimation algorithm for the unknown parameters under both hypotheses. Then, we propose a generalized likelihood ratio test that employs the approximate estimates. Finally, we evaluate the performance of the proposed detector under Gaussian disturbance and verify its advantage to some existing techniques.

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Notes

  1. The radar sends a succession of N periodic pulses, i.e., N repetitions of a waveform, over a coherent processing interval (CPI).

  2. The covariance matrices are Hermitian. For a general \(N \times N\) complex Hermitian matrix, there is a total of \((N\times N+N)/2\) free complex parameters.

References

  1. A. Aubry, V. Carotenuto, A. De Maio, G. Foglia, Exploiting multiple a priori spectral models for adaptive radar detection. IET Radar Sonar Navig. 8(7), 695–707 (2013)

    Article  Google Scholar 

  2. A. Aubry, A. De Maio, L. Pallotta, A. Farina, Radar detection of distributed targets in homogeneous interference whose inverse covariance structure is defined via unitary invariant functions. IEEE Trans. Signal Process. 61(20), 4949–4961 (2013)

    Article  MathSciNet  Google Scholar 

  3. G. Alfano, A. De Maio, A. Farina, Model-based adaptive detection of range-spread targets. IEE Proc. Radar Sonar Navig. 151(1), 2–10 (2004)

    Article  Google Scholar 

  4. C.G. Backman, Some recent developments in RCS measurements techniques. Proc. IEEE 53(8), 962–972 (1965)

    Article  Google Scholar 

  5. F. Bandiera, A. De Maio, A.S. Greco, G. Ricci, Adaptive radar detection of distributed targets in homogeneous and partially homogeneous noise plus subspace interference. IEEE Trans. Signal Process. 55(4), 1223–1237 (2007)

    Article  MathSciNet  Google Scholar 

  6. F. Bandiera, O. Besson, D. Orlando, G. Ricci, L.L. Scharf, GLRT based direction detectors in homogeneous noise and subspace interference. IEEE Trans. Signal Process. 55(6), 2386–2394 (2007)

    Article  MathSciNet  Google Scholar 

  7. S. Bose, A.O. Steinhardt, Adaptive array detection of uncertain rank one waveforms. IEEE Trans. Signal Process. 44(11), 2801–2808 (1996)

    Article  Google Scholar 

  8. F. Bandiera, O. Besson, G. Ricci, Adaptive detection of distributed targets in compound-Gaussian noise without secondary data: a Bayesian a pproach. IEEE Trans. Signal Process. 59(12), 5698–5708 (2011)

    Article  MathSciNet  Google Scholar 

  9. V. Carotenuto, A. De Maio, A. Aubry, G. Foglia, Adaptive radar detection based on multiple a-priori models, in Proceedings IEEE radar Conference, Ottawa, ON, USA (2013), pp. 1–5

  10. E. Conte, A. De Maio, G. Ricci, GLRT-based adaptive detection algorithms for range-spread targets. IEEE Trans. Signal Process. 49(7), 1336–1348 (2001)

    Article  Google Scholar 

  11. G. Cui, L. Kong, X. Yang, Performance analysis of colocated MIMO radars with randomly distributed arrays in compound-Gaussian Clutter. Circuits Syst. Signal Process. 31(4), 1407–1422 (2012)

    Article  MathSciNet  Google Scholar 

  12. A. De Maio, G. Foglia, A. Farina, M. Piezzo, Estimation of the covariance matrix based on multiple a-priori models, in Proceedings IEEE radar Conference, Washington, DC, USA (2010), pp. 1025–1029

  13. A. Farina, F. Scannapieco, F. Vinelli, Target detection and classification with very high range resolution radar, in Proceedings of International Conference on Radar, Versailles, France (1989), pp. 20–25N

  14. A. Farina, F. Gini, M.V. Greco, P.H.Y. Lee, Improvement factor for real sea-clutter doppler frequency spectra. IEE Proc. Radar Sonar Navig. 143(5), 341–344 (1996)

    Article  Google Scholar 

  15. K. Gerlach, M.J. Steiner, Adaptive detection of range distributed targets. IEEE Trans. Signal Process. 47(7), 1844–1851 (1999)

    Article  Google Scholar 

  16. C. Hao, D. Orlando, G. Foglia, X. Ma, S. Yan, C. Hou, Persymmetric adaptive detection of distributed targets in partially-homogeneous environment. Digital Signal Process. 24(1), 42–51 (2014)

    Article  MathSciNet  Google Scholar 

  17. C. Hao, J. Yang, X. Ma, C. Hou, D. Orlando, Adaptive detection of distributed targets with orthogonal rejection. IET Radar Sonar Navig. 6(6), 483–493 (2012)

    Article  Google Scholar 

  18. E.J. Kelly, An adaptive detection algorithm. IEEE Trans. Aerosp. Electron. Syst. 2(1), 115–127 (1986)

    Article  Google Scholar 

  19. S.M. Kay, Fundamentals of Statistical Signal Processing, Volume II: Detection Theory (Prentice-Hall, Upper Saddle River, 1998)

    Google Scholar 

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

    Article  Google Scholar 

  21. T.T. Moon, P.J. Bawden, High resolution RCS measurements of boats. IEE Proc. Pt. F. Radar Signal Process. 138(3), 218–222 (1991)

    Article  Google Scholar 

  22. W.L. Melvin, Space–time adaptive radar performance in heterogeneous clutter. IEEE Trans. Aerosp. Electron. Syst. 36(2), 621–633 (2000)

    Article  Google Scholar 

  23. A. Nemirovski, Lectures on modern convex optimization. http://www.isye.gatech.edu/faculty-staff/profile.php?entry=an63

  24. I.S. Reed, J.D. Mallett, L.E. Brennan, Rapid convergence rate in adaptive arrays. IEEE Trans. Aerosp. Electron. Syst. 10(4), 853–863 (1974)

    Article  Google Scholar 

  25. C.D. Richmond, Performance of a class of adaptive detection algorithms in nonhomogeneous environments. IEEE Trans. Signal Process. 48(5), 1248–1262 (2000)

    Article  Google Scholar 

  26. F.C. Robey, D.R. Fuhrmann, R. Nitzberg, E.J. Kelly, A CFAR adaptive matched filter detector. IEEE Trans. Aerosp. Electron. Syst. 28(1), 208–216 (1992)

    Article  Google Scholar 

  27. X. Shuai, L. Kong, J. Yang, AR-model-based adaptive detection of range-spread targets in compound Gaussian clutter. Signal Process. 91(4), 750–758 (2011)

    Article  MATH  Google Scholar 

  28. H.L. Van Trees, Detection, Estimation, and Modulation Theory (Wiley, New York, 1968)

    MATH  Google Scholar 

  29. L. Vandenberghe, S. Boyd, S.P. Wu, Determinant maximization with linear matrix inequality constraints. SIAM J. Matrix Anal. 19(2), 499–533 (1998)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgments

This work was sponsored by National Natural Science Foundation of China (61301266, 61178068 and 61201276) and by Fundamental Research Funds of Central Universities (ZYGX2012YB005 and ZYGX2012Z001) and by Program for New Century Excellent Talents in University (A1098524023901001063).

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Authors and Affiliations

  1. School of Electronic Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu City, China

    Yun Zhou, Haining Yang, Na Li, Guolong Cui & Lingjiang Kong

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  1. Yun Zhou
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  2. Haining Yang
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  3. Na Li
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  4. Guolong Cui
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  5. Lingjiang Kong
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Correspondence to Na Li.

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Zhou, Y., Yang, H., Li, N. et al. Adaptive Detection of Distributed Targets in Gaussian Clutter Based on Multiple A-Priori Spectral Models. Circuits Syst Signal Process 36, 420–434 (2017). https://doi.org/10.1007/s00034-016-0300-0

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