Skip to main content
SpringerLink
Log in

Performance Analysis of Adaptive Detectors for a Distributed Target Based on Subspace Model

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

Abstract

For the problem of detecting a distributed target using a subspace model, we examine one nonadaptive detector and three adaptive detectors. The nonadaptive detector was proposed under the assumption that the noise covariance matrix is known a priori, i.e., the detector is the asymptotic optimum detector (AOD). We derive the statistical distribution of the AOD, according to which we obtain analytical expressions for the probabilities of detection and false alarm. Many kinds of simulations are carried out, from which useful results are obtained.

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

Similar content being viewed by others

References

  1. F. Bandiera, O. Besson, D. Orlando, G. Ricci, Theoretical performance analysis of the W-ABORT detector. IEEE Trans. Signal Process. 56(5), 2117–2121 (2008)

    Article  MathSciNet  Google Scholar 

  2. 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 

  3. S. Chen, L. Kong, J. Yang, Target detection for heterogeneous cyclostationary sea clutter. Circuits Syst. Signal Process. 33(3), 959–971 (2014)

    Article  Google Scholar 

  4. X. Chen, J. Guan, Y. Huang, N. Liu, Y. He, Radon-linear canonical ambiguity function-based detection and estimation method for marine target with micromotion. IEEE Trans. Geosci. Remote Sens. 53(4), 2225–2240 (2015)

    Article  Google Scholar 

  5. D. Ciuonzo, A. De Maio, D. Orlando, A unifying framework for adaptive radar detection in homogeneous plus structured interference—part I: on the maximal invariant statistic. IEEE Trans. Signal Process. 64(11), 2894–2906 (2016)

    Article  MathSciNet  Google Scholar 

  6. D. Ciuonzo, A. De Maio, D. Orlando, A unifying framework for adaptive radar detection in homogeneous plus structured interference—part II: detectors design. IEEE Trans. Signal Process. 64(11), 2907–2919 (2016)

    Article  MathSciNet  Google Scholar 

  7. 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 

  8. 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 

  9. 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 

  10. A. De Maio, S.M. Kay, A. Farina, On the invariance, coincidence, and statistical equivalence of the GLRT, Rao test, and Wald test. IEEE Trans. Signal Process. 58(4), 1967–1979 (2010)

    Article  MathSciNet  Google Scholar 

  11. G.A. Fabrizio, A. Farina, M.D. Turley, Spatial adaptive subspace detection in OTH radar. IEEE Trans. Aerosp. Electron. Syst. 39(4), 1407–1428 (2003)

    Article  Google Scholar 

  12. Y. Gao, H. Li, B. Himed, Knowledge-aided range-spread target detection for distributed MIMO radar in nonhomogeneous environments. IEEE Trans. Signal Process. 65(3), 617–627 (2017)

    Article  MathSciNet  Google Scholar 

  13. C. Hao, B. Liu, L. Cai, Performance analysis of a two-stage Rao detector. Sig. Process. 91(8), 2141–2146 (2011)

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  15. C. Hao, D. Orlando, X. Ma, C. Hou, Persymmetric Rao and Wald tests for partially homogeneous environment. IEEE Signal Process. Lett. 19(9), 587–590 (2012)

    Article  Google Scholar 

  16. C. Hao, D. Orlando, X. Ma, S. Yan, C. Hou, Persymmetric detectors with enhanced rejection capabilities. IET Radar Sonar Navig. 8(5), 557–563 (2014)

    Article  Google Scholar 

  17. E.J. Kelly, K.M. Forsythe, Adaptive detection and parameter estimation for multidimensional signal models. Technical report, Lincoln Laboratory, Lexington (1989)

  18. E.J. Kelly, An adaptive detection algorithm. IEEE Trans. Aerosp. Electron. Syst. 22(1), 115–127 (1986)

    Article  Google Scholar 

  19. S. Kraut, L.L. Scharf, The CFAR adaptive subspace detector is a scale-invariant GLRT. IEEE Trans. Signal Process. 47(9), 2538–2541 (1999)

    Article  Google Scholar 

  20. S. Kraut, L.L. Scharf, Adaptive subspace detectors. IEEE Trans. Signal Process. 49(1), 1–16 (2001)

    Article  Google Scholar 

  21. J. Liu, G. Cui, H. Li, B. Himed, On the performance of a persymmetric adaptive matched filter. IEEE Trans. Aerosp. Electron. Syst. 51(4), 2605–2614 (2015)

    Article  Google Scholar 

  22. J. Liu, W. Liu, B. Chen, H. Liu, H. Li, C. Hao, Modified Rao test for multichannel adaptive signal detection. IEEE Trans. Signal Process. 64(3), 714–725 (2016)

    Article  MathSciNet  Google Scholar 

  23. J. Liu, Z.-J. Zhang, Y. Yang, Optimal waveform design for generalized likelihood ratio and adaptive matched filter detectors using a diversely polarized antenna. Sig. Process. 92(4), 1126–1131 (2012)

    Article  Google Scholar 

  24. J. Liu, Z.-J. Zhang, Y. Yang, H. Liu, A CFAR adaptive subspace detector for first-order or second-order Gaussian signals based on a single observation. IEEE Trans. Signal Process. 59(11), 5126–5140 (2011)

    Article  MathSciNet  Google Scholar 

  25. W. Liu, J. Liu, L. Huang, Z. Yang, H. Yang, Y.-L. Wang, Distributed target detectors with capabilities of mismatched subspace signal rejection. IEEE Trans. Aerosp. Electron. Syst. 53(2), 888–900 (2017)

    Article  Google Scholar 

  26. W. Liu, Y. Wang, J. Liu, W. Xie, R. Li, H. Chen, Design and performance analysis of adaptive subspace detectors in orthogonal interference and Gaussian noise. IEEE Trans. Aerosp. Electron. Syst. 52(5), 2068–2079 (2016)

    Article  Google Scholar 

  27. W. Liu, W. Xie, J. Liu, Y. Wang, Adaptive double subspace signal detection in Gaussian backgroundpart I: homogeneous environments. IEEE Trans. Signal Process. 62(9), 2345–2357 (2014)

    Article  MathSciNet  Google Scholar 

  28. W. Liu, W. Xie, J. Liu, Y. Wang, Adaptive double subspace signal detection in Gaussian backgroundpart II: partially homogeneous environments. IEEE Trans. Signal Process. 62(9), 2358–2369 (2014)

    Article  MathSciNet  Google Scholar 

  29. D. Orlando, G. Ricci, Adaptive radar detection and localization of a point-like target. IEEE Trans. Signal Process. 59(9), 4086–4096 (2011)

    Article  MathSciNet  Google Scholar 

  30. D. Pastina, P. Lombardo, T. Bucciarelli, Adaptive polarimetric target detection with coherent radar part I: detection against Gaussian background. IEEE Trans. Aerosp. Electron. Syst. 37(4), 1194–1206 (2001)

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. D.A. Shnidman, The calculation of the probability of detection and the generalized marcum q-function. IEEE Trans. Inf. Theory 35(2), 389–400 (1989)

    Article  MATH  Google Scholar 

  33. X. Shuai, L. Kong, J. Yang, Adaptive detection for distributed targets in Gaussian noise with Rao and Wald tests. Sci. China Inf. Sci. 55(6), 1290–1300 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  34. J. Xu, X. Zhou, L.-C. Qian, X.-G. Xia, T. Long, Hybrid integration for highly maneuvering radar target detection based on generalized Radon-Fourier transform. IEEE Aerosp. Electron. Syst. Mag. 52(5), 2554–2561 (2016)

    Article  Google Scholar 

  35. T. Zhang, G. Cui, L. Kong, X. Yang, Adaptive Bayesian detection using MIMO radar in spatially heterogeneous clutter. IEEE Signal Process. Lett. 20(6), 547–550 (2013)

    Article  Google Scholar 

  36. J.-D. Zhu, J.-L. Li, X.-D. Gao, L.-B. Ye, H.-Y. Dai, Adaptive threshold detection and estimation of linear frequency-modulated continuous-wave signals based on periodic fractional Fourier transform. Circuits Syst. Signal Process. 35(7), 2502–2517 (2016)

    Article  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61501505 and Natural Science Foundation of Hubei Province, China, under Grant 2017CFB589. We thank Glenn Pennycook, MSc, from Liwen Bianji, Edanz Group China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.

Author information

Authors and Affiliations

  1. Wuhan Electronic Information Institute, Wuhan, 430019, China

    Hongli Li, Huan Wang & Weijian Liu

  2. State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang, 471003, China

    Hui Han & Huanyao Dai

Authors
  1. Hongli Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hui Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huanyao Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Weijian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weijian Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, H., Wang, H., Han, H. et al. Performance Analysis of Adaptive Detectors for a Distributed Target Based on Subspace Model. Circuits Syst Signal Process 37, 2651–2664 (2018). https://doi.org/10.1007/s00034-017-0688-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-017-0688-1

Keywords

Search

Navigation