Skip to main content
SpringerLink
Log in

MIMO Radar Moving Target Detection Against Compound-Gaussian Clutter

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

This paper focuses on the moving target detection (MTD) problem for multiple-input multiple-output (MIMO) radar in the presence of compound-Gaussian clutter. By exploiting the spatial and velocity diversities, we devise two different generalized likelihood ratio tests (GLRTs) according to the centralized and distributed processing schemes of MIMO radar systems, respectively. Then, we investigate the fully adaptive detectors, where the covariance matrix is replaced by a suitable estimator based on the secondary data. Finally, we provide several numerical simulations with typical parameters, and the results illustrate that the newly proposed detectors can provide much better detection performance in spikier clutter for moving targets than the phased-array counterpart and those obtained in Gaussian environment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. V. Anastassopoulos, G.A. Lampropoulos, A. Drosopoulos, M. Ray, High resolution radar clutter statistics. IEEE Trans. Aerosp. Electron. Syst. 35(1), 43–60 (1999)

    Article  Google Scholar 

  2. M. Akcakaya, A. Nehorai, MIMO radar sensitivity analysis for target detection. IEEE Trans. Signal Process. 59(7), 3241–3250 (2011)

    Article  MathSciNet  Google Scholar 

  3. M. Akcakaya, A. Nehorai, Adaptive MIMO radar design and detection in compound-Gaussian clutter. IEEE Trans. Aerosp. Electron. Syst. 47(3), 2200–2207 (2011)

    Article  Google Scholar 

  4. G. Cui, L. Kong, X. Yang, Multiple-input multiple-output radar detectors design in non-Gaussian clutter. IET Radar Sonar Navig. 4(5), 724–732 (2010)

    Article  Google Scholar 

  5. G. Cui, L. Kong, X. Yang, GLRT-based detection algorithm for polarimetric MIMO radar against SIRV clutter. Circuits, Syst. Signal Process. 31(3), 1033–1048 (2012)

    Article  MathSciNet  Google Scholar 

  6. G. Cui, L. Kong, X. Yang, J. Yang, The Rao and Wald tests designed for distributed targets with polarization MIMO radar in compound-Gaussian clutter. Circuits, Syst. Signal Process. 31(1), 237–254 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  7. G.R. Curry, Radar System Performance Modeling, 2nd edn. (Artech House, Norwood, 2005)

    Google Scholar 

  8. E. Conte, M. Longo, M. Lops, Modelling and simulation of non-Rayleigh radar clutter. IEE Proc., F 138(2), 121–130 (1991)

    Google Scholar 

  9. E. Conte, M. Longo, Characterisation of radar clutter as a spherically invariant random process. IEE Proc., F. 134(2), 191–197 (1987)

    Google Scholar 

  10. E. Conte, M. Lops, G. Ricci, Adaptive matched filter detection in spherically invariant noise. IEEE Signal Process. Lett. 3(8), 248–250 (1996)

    Article  Google Scholar 

  11. E. Conte, A. De Maio, G. Ricci, Recursive estimation of the covariance matrix of a compound-Gaussian process and its application to adaptive CFAR detection. IEEE Trans. Signal Process. 50(8), 1908–1915 (2002)

    Article  Google Scholar 

  12. A. De Maio, M. Lops, Design principles of MIMO radar detectors. IEEE Trans. Aerosp. Electron. Syst. 43(3), 886–898 (2007)

    Article  Google Scholar 

  13. A. De Maio, G. Alfano, E. Conte, Polarization diversity detection in compound-Gaussian clutter. IEEE Trans. Aerosp. Electron. Syst. 40(1), 114–131 (2004)

    Article  Google Scholar 

  14. E. Fishler, A.M. Haimovich, R.S. Blum, L.J. Cimini, D. Chizhik, R.A. Valenzuela, Spatial diversity in radars-models and detection performance. IEEE Trans. Signal Process. 54(3), 823–838 (2006)

    Article  Google Scholar 

  15. J.R. Guerci, Space-Time Adaptive Processing for Radar (Artech House, Norwood, 2003)

    Google Scholar 

  16. F. Gini, Performance analysis of two structured covariance matrix estimators in compound-Gaussian clutter. Signal Process. 80(2), 365–371 (2000)

    Article  MATH  Google Scholar 

  17. F. Gini, M.V. Greco, Covariance matrix estimation for CFAR detection in correlated heavy tailed clutter. Signal Process. 82(12), 1847–1859 (2002)

    Article  Google Scholar 

  18. A.M. Haimovich, R.S. Blum, L.J. Cimini, MIMO radar with widely separated antennas. IEEE Signal Process. Mag. 25(1), 116–129 (2008)

    Article  Google Scholar 

  19. Q. He, N.H. Lehmann, R.S. Blum, A. Haimovich, MIMO radar moving target detection in homogeneous clutter. IEEE Trans. Aerosp. Electron. Syst. 46(3), 1290–1301 (2010)

    Article  Google Scholar 

  20. R. Klemm, Applications of Space-Time Adaptive Processing (IEE Press, London, 2004)

    Book  Google Scholar 

  21. E.J. Kelly, An adaptive detection algorithm. IEEE Trans. Aerosp. Electron. Syst. 22(2), 115–127 (1986)

    Article  Google Scholar 

  22. J. Li, P. Stoica, MIMO Radar Signal Processing (Wilely, New York, 2009)

    Google Scholar 

  23. J. Li, P. Stoica, MIMO radar with colocated antennas. IEEE Signal Process. Mag. 24(5), 106–114 (2007)

    Article  Google Scholar 

  24. J. Li, P. Stoica, L. Xu, W. Roberts, On parameter identifiability of MIMO radar. IEEE Signal Process. Lett. 14(12), 968–971 (2007)

    Article  Google Scholar 

  25. N. Li, G. Cui, L. Kong, X. Yang, Rao and Wald tests design of multiple-input multipleoutput radar in compound-Gaussian clutter. IET Radar Sonar Navig. 6(8), 729–738 (2012)

    Article  Google Scholar 

  26. N.H. Lehmann, A.M. Haimovich, R.S. Blum, L.J. Cimini, MIMO radar application to moving target detection in homogenous clutter, in Proceedings of 14th Adaptive Sensor Array Processing Workshop (MIT Lincoln Laboratory, New York, 2006), pp. 1–23

    Google Scholar 

  27. N.H. Lehmann, A.M. Haimovich, R.S. Blum, L.J. Cimini, High resolution capabilities of MIMO radar, in Proceedings of 40th Asilomar Conference Signals, Systems and Computers, Pacific Grove, CA, USA (2006), pp. 25–30

    Google Scholar 

  28. F. Pascal, Y. Chitour, J.-P. Ovarlez, P. Forster, P. Larzabal, Covariance structure maximum-likelihood estimates in compound Gaussian noise: existence and algorithm analysis. IEEE Trans. Signal Process. 56(1), 34–48 (2008)

    Article  MathSciNet  Google Scholar 

  29. M. Rangaswamy, D.D. Weiner, A. Ozturk, Non-Gaussian vector identification using spherically invariant random processes. IEEE Trans. Aerosp. Electron. Syst. 29(1), 111–124 (1993)

    Article  Google Scholar 

  30. K. Shadi, F. Behnia, MIMO radar beamforming using orthogonal decomposition of correlation matrix. Circuits, Syst. Signal Process. 32(4), 1791–1809 (2013)

    Article  Google Scholar 

  31. M.I. Skolnik, Introduction to Radar Systems, 3rd edn. (McGraw-Hill, New York, 2001)

    Google Scholar 

  32. D.N. Vizireanu, R.O. Preda, Is “five-point” estimation better than “three-point” estimation? Measurement 46(1), 840–842 (2013)

    Article  Google Scholar 

  33. P. Wang, H. Li, B. Himed, Moving target detection using distributed MIMO radar in clutter with non-homogeneous power. IEEE Trans. Signal Process. 59(10), 4809–4820 (2011)

    Article  MathSciNet  Google Scholar 

  34. P. Wang, H. Li, B. Himed, Distributed detection of moving target using MIMO radar in clutter with non-homogeneous power, in Proceedings of 7th International Workshop on Systems, Signal Processing and Their Applications, Tipaza, Algeria (2011), pp. 383–387

    Chapter  Google Scholar 

  35. P. Wang, H. Li, B. Himed, Centralized and distributed tests for moving target detection with MIMO radars in clutter of non-homogeneous power, in Proceedings of 45th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA (2011), pp. 878–882

    Google Scholar 

  36. P. Wang, H. Li, B. Himed, Recursive moving target detection with distributed MIMO radar in clutter with non-homogeneous power, in Proceedings of IEEE Radar Conference, Atlanta, GA, USA (2012), pp. 504–509

    Google Scholar 

  37. K.D. Ward, C.J. Baker, S. Watts, Maritime surveillance radar. Part 1: Radar scattering from the ocean surface. IEE Proc., F 137(2), 51–62 (1990)

    Google Scholar 

  38. L. Xu, J. Li, P. Stoica, Adaptive techniques for MIMO radar, in Proceedings of 4th IEEE Workshop on Sensor Array and Multi-Channel Processing, Waltham, MA, USA (2006), pp. 258–262

    Google Scholar 

Download references

Acknowledgements

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).

Author information

Authors and Affiliations

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

    Na Li, Guolong Cui, Lingjiang Kong & Xiaobo Yang

Authors
  1. Na Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guolong Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lingjiang Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaobo Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lingjiang Kong.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, N., Cui, G., Kong, L. et al. MIMO Radar Moving Target Detection Against Compound-Gaussian Clutter. Circuits Syst Signal Process 33, 1819–1839 (2014). https://doi.org/10.1007/s00034-013-9718-9

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-013-9718-9

Keywords

Search

Navigation