Skip to main content
SpringerLink
Log in

Online source number estimation based on sequential hypothesis test and subspace tracking

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

We investigated the problem of source enumeration in array signal processing. The conventional batch estimating methods do not yield satisfactory tracking performance in a dynamic environment. In order to solve this problem, an online source number estimation method is proposed in this paper. The developed algorithm exploits subspace tracking and hypothesis test to update the estimation of the signal number sequentially. Simulation results validate the superiority of the new method in terms of tracking capacity and computation efficiency.

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

Similar content being viewed by others

References

  1. Wu, L., Liu, Z., Jiang, W.: A direction finding method for spatial optical beam-forming network based on sparse Bayesian learning. Signal Image Video Process 11(2), 203–209 (2017)

    Article  Google Scholar 

  2. Ta, S., Wang, H.: Propagator-based computationally efficient direction finding via low-dimensional equation rooting. Signal Image Video Process 12(1), 83–90 (2018)

    Article  Google Scholar 

  3. Salvati, D., Drioli, C., Foresti, G.L.: Frequency map selection using a RBFN-based classifier in the MVDR beamformer for speaker localization in reverberant rooms. In: INTERSPEECH 2015, 16th Annual Conference of the International Speech Communication Association, pp. 3298–3301, Dresden, Germany (2015)

  4. Ma, L., Tsoi, A.C.: A variational bayesian approach to number of sources estimation for multichannel blind deconvolution. Signal Image Video Process 2(2), 107–127 (2018)

    Article  MATH  Google Scholar 

  5. Kritchman, S., Nadler, B.: Non-parametric detection of the number of signals: hypothesis testing and random matrix theory. IEEE Trans Signal Process 57(10), 3930–3941 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  6. Akaike, H.: A new look at the statistical model identification. IEEE Trans Autom Control 19(6), 716–723 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  7. Schwarz, G.: Estimating the dimension of a model. Ann Stat 6(2), 15–18 (1978)

    Article  MathSciNet  Google Scholar 

  8. Fishler, E., Poor, H.V.: Estimation of the number of sources in unbalanced arrays via information theoretic criteria. IEEE Trans Signal Process 53(9), 3543–3553 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  9. Wong, M.K., Zhang, Q.T., Reilly, J.P., Yip, P.C.: On information theoretic criteria for determining the number of signals in high resolution array processing. IEEE Trans Signal Process 38(11), 1959–1971 (1990)

    Article  MATH  Google Scholar 

  10. Tsinos, C.G., Berberidis, K.: Decentralized adaptive eigenvalue-based spectrum sensing for multiantenna cognitive radio systems. IEEE Trans Wirel Commun 14(3), 1703–1716 (2015)

    Article  Google Scholar 

  11. Zou, Q., Zheng, S., Sayed, A.: Cooperative sensing via sequential detection. IEEE Trans Signal Process 58(12), 6266–6283 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  12. Renard, J., Lampe, L., Horlin, F.: Sequential likelihood ratio test for cognitive radios. IEEE Trans Signal Process 64(24), 6627–6639 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  13. Zeng, Y., Liang, Y.C.: Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Trans Commun 57(6), 1784–1793 (2009)

    Article  Google Scholar 

  14. Kortun, A., Ratnarajah, T., Sellathurai, M., Zhong, C., Papadias, C.: On the performance of eigenvalue-based cooperative spectrum sensing for cognitive radio. IEEE J Sel Top Signal Process 5(1), 49–55 (2011)

    Article  Google Scholar 

  15. Yang, B.: Projection approximation subspace tracking. IEEE Trans Signal Process 43(1), 95–107 (1995)

    Article  Google Scholar 

  16. Abed-Meraim, K., Chkeif, A., Hua, Y.: Fast orthonormal past algorithm. IEEE Signal Process Lett 7(3), 60–63 (2000)

    Article  Google Scholar 

  17. Miao, Y., Hua, Y.: Fast subspace tracking and neural network learning by a novel information criterion. IEEE Trans Signal Process 46(7), 1967–1980 (1998)

    Article  Google Scholar 

  18. Doukopoulos, X., Moustakides, G.: Fast and stable subspace tracking. IEEE Trans Signal Process 56(4), 1452–1465 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  19. Badeau, R., David, B., Richard, G.: Fast approximated power iteration subspace tracking. IEEE Trans Signal Process 53(8), 2931–2941 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  20. Doukopoulos, X.: Power techniques for blind channel estimation in wireless communications systems. Ph.D. Thesis, IRISA-INRIA, University of Rennes, France (2004)

  21. Oja, E.: A simplified neuron model as a principal component analyzer. J Math Biol 15(3), 267–273 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  22. Abed-Meraim, K., Attallah, S., Chkeif, A., Hua, Y.: Orthogonal Oja algorithm. IEEE Signal Process Lett 7(5), 116–120 (2000)

    Article  Google Scholar 

  23. Kavcic, A., Yang, B.: Adaptive rank estimation for spherical subspace trackers. IEEE Trans Signal Process 44(6), 1573–1579 (1996)

    Article  Google Scholar 

  24. Yang, B.: An extension of the pastd algorithm to both rank and subspace tracking. IEEE Signal Process Lett 2(9), 179–182 (1995)

    Article  Google Scholar 

Download references

Acknowledgements

The work is supported by the National Natural Science Foundation of China (No. 61302141).

Author information

Authors and Affiliations

  1. State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha, 410073, Hunan, People’s Republic of China

    Liu-Li Wu, Zhang-meng Liu & Zhi-tao Huang

Authors
  1. Liu-Li Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhang-meng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhi-tao Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liu-Li Wu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, LL., Liu, Zm. & Huang, Zt. Online source number estimation based on sequential hypothesis test and subspace tracking. SIViP 13, 307–311 (2019). https://doi.org/10.1007/s11760-018-1358-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-018-1358-x

Keywords

Search

Navigation