Skip to main content

Detection Performance Analysis of Tests for Spread Targets in Compound-Gaussian Clutter

  • Conference paper
Book cover Information Computing and Applications (ICICA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7473))

Included in the following conference series:

  • 4760 Accesses

Abstract

The problem of adaptive detection of spatially distributed targets or targets embedded in compound-Gaussian clutter with unknown covariance matrix is studied. At first, the test decision statistic of the generalized likelihood ratio test (GLRT), Rao test and Wald test which have been widely applied to the distributed targets detection of modern Wideband radar is derived. Next, the numerical results are presented by means of Monte Carlo simulation strategy. Assume that cells of signal components are available. Those secondary data are supposed to possess either the same covariance matrix or the same structure of the covariance matrix of the cells under test. In this context, the simulation results highlight that the asymptotic properties of the three tests in different coherent train pulses, and that the performance loss of the real target length mismatches the setup in receiver.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Conte, E., De Maio, A., Ricci, G.: GLRT-based adaptive detection algorithms for range-spread targets. IEEE Transactions on Signal Processing 49(7), 1336–1348 (2001)

    Article  Google Scholar 

  2. Conte, E., De Maio, A., Galdi, C.: Signal detection in compound-Gaussian noise: Neyman–Pearson and CFAR detectors. IEEE Transactions on Signal Processing 48(2), 419–428 (2000)

    Article  Google Scholar 

  3. De Maio, A., Iommelli, S.: Coincidence of the Rao Test, Wald Test, and GLRT in Partially Homogeneous Environment. IEEE Signal Processing Letters 15 (2008)

    Google Scholar 

  4. Bon, N., Khenchaf, A., Garello, R.: GLRT subspace detection for range and doppler distributed targets. IEEE Transactions on Aerospace and Electronic Systems 44(2), 678–695 (2008)

    Article  Google Scholar 

  5. Robey, F.C., Fuhrmann, D.R., Nitzberg, R., Kelly, E.J.: A CFAR adaptive matched filter detector. IEEE Transactions on Aerospace and Electronic Systems 28(1), 208–216 (1992)

    Article  Google Scholar 

  6. Conte, E., De Maio, A., Ricci, G.: Asymptotically optimum radar detection in compound Gaussian clutter. IEEE Transactions on Aerospace and Electronic Systems 31(2), 617–625 (1995)

    Article  Google Scholar 

  7. Bandiera, F., De Maio, A., Ricci, G.: Adaptive Radar Detection of Distributed Targets in Homogeneous and Partially Homogeneous Noise Plus Subspace Interference. IEEE Transactions on Signal Processing 55(4) (April 2007)

    Google Scholar 

  8. Gini, F., Farina, A.: Vector subspace detection in compound-Gaussian clutter, part I: survey and new results. IEEE Transactions on Aerospace and Electronic Systems 38, 1295–1311 (2002)

    Article  Google Scholar 

  9. Shuai, X., Kong, L., Yang, J.: Performance analysis of GLRT-based adaptive detector for distributed targets in compound-Gaussian clutter. Signal Processing 90, 16–23 (2010)

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  11. Pascal, F., Chitour, Y., Forster, P., Larzabal, P.: Covariance structure maximum-likelihood estimates in compound Gaussian noise existence and algorithm analysis. IEEE Transactions on Signal Processing 56(1), 34–48 (2008)

    Article  MathSciNet  Google Scholar 

  12. CFAR adaptive subspace detector is a scale-invariant GLRT. IEEE Trans. on Sign. Process 47, 2538–2541 (1999)

    Google Scholar 

  13. Shuai, X.: Research on Detection Algorithms of Range-Targets. Doctoral Dissertation, 68–77 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Meng, X., He, Z., Niu, X., Feng, G. (2012). Detection Performance Analysis of Tests for Spread Targets in Compound-Gaussian Clutter. In: Liu, B., Ma, M., Chang, J. (eds) Information Computing and Applications. ICICA 2012. Lecture Notes in Computer Science, vol 7473. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34062-8_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-34062-8_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34061-1

  • Online ISBN: 978-3-642-34062-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics