Skip to main content

Advertisement

Springer Nature Link
Log in

Improving the Efficiency of Natural Computing Algorithms in DOA Estimation Using a Noise Filtering Approach

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

Abstract

We propose a novel strategy to generate initial candidate solutions for bio-inspired algorithms applied to the direction of arrival estimation problem. The idea, which aims to improve the efficiency of the estimator, consists in using the frequency response of a well-known optimum noise reduction filter as the probability density function of the set of candidate solutions. In accordance to this approach, we also employ a modified likelihood function to reduce the estimation error. Simulation results considering an immune-inspired algorithm confirm a significant improvement of its performance and efficiency, and the new estimator reaches the conditional Cramér–Rao lower bound.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Algorithm 1

Similar content being viewed by others

References

  1. L. Boccato, R. Krummenauer, R. Attux, A. Lopes, Application of natural computing algorithms to maximum likelihood estimation of direction of arrival. Signal Process. 92(5), 1338–1352 (2012)

    Article  Google Scholar 

  2. L.N. de Castro, F.J. Von Zuben, Learning and optimization using the clonal selection principle. IEEE Trans. Evol. Comput. 6(3), 239–251 (2002)

    Article  Google Scholar 

  3. A. Gershman, P. Stoica, MODE with extra-roots (MODEX): a new DOA estimation algorithm with an improved threshold performance, in IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, vol. 5, (1999), pp. 2833–2836

    Google Scholar 

  4. R. Krummenauer, M. Cazarotto, A. Lopes, P. Larzabal, P. Forster, Improving the threshold performance of maximum likelihood estimation of direction of arrival. Signal Process. 90(5), 1582–1590 (2010)

    Article  MATH  Google Scholar 

  5. P. Larrañaga, J.A. Lozano, Estimation of Distribution Algorithms: a New Tool for Evolutionary Computation. Genetic Algorithms and Evolutionary Computation. (Kluwer Academic, Norwell, 2002)

    Book  Google Scholar 

  6. A. Lopes, I.S. Bonatti, P.L.D. Peres, C.A. Alves, Improving the MODEX algorithm for direction estimation. Signal Process. 83(9), 2047–2051 (2003). doi:10.1016/S0165-1684(03)00146-4

    Article  MATH  Google Scholar 

  7. K.C. Sharman, G.D. McGlurkin, Genetic algorithms for maximum likelihood parameter estimation, in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, (1989), pp. 2716–2719

    Chapter  Google Scholar 

  8. P. Stoica, A.B. Gershman, Maximum-likelihood DOA estimation by data-supported grid search, in IEEE Signal Processing Letters, (1999), pp. 273–275

    Google Scholar 

  9. P. Stoica, A. Nehorai, Performance study of conditional and unconditional direction-of-arrival estimation. IEEE Trans. Acoust. Speech Signal Process. 38(10), 1783–1795 (1990)

    Article  MATH  Google Scholar 

  10. P. Stoica, K.C. Sharman, Novel eigenanalysis method for direction estimation, in IEE Proceedings part F (Radar and Signal Processing), vol. 137, (1990)

    Google Scholar 

  11. H.L. Van Trees, Optimum Array Processing. Part IV of Detection, Estimation and Modulation Theory (Wiley, New York, 2001)

    Book  Google Scholar 

  12. D.N. Vizireanu, A fast, simple and accurate time-varying frequency estimation method for single-phase electric power systems. Measurement 45(5), 1331–1333 (2012)

    Article  Google Scholar 

  13. D.N. Vizireanu, S.V. Halunga, Simple, fast and accurate eight points amplitude estimation method of sinusoidal signals for dsp based instrumentation. J. Instrum. (2012). doi:10.1088/1748-0221/7/04/P04001

    Google Scholar 

Download references

Acknowledgements

This work was sponsored by CNPq and FAPESP (2008/56937-2, 2010/51027-8), Brazil.

Author information

Authors and Affiliations

  1. School of Electrical and Computer Engineering (FEEC), University of Campinas (UNICAMP), 13083-852, São Paulo, Brazil

    L. Boccato, R. Krummenauer, R. Attux & A. Lopes

Authors
  1. L. Boccato
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Krummenauer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Attux
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to L. Boccato.

Appendix

Appendix

In the following, we provide the pseudocode of the algorithm CLONALG, described in Sect. 4.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Boccato, L., Krummenauer, R., Attux, R. et al. Improving the Efficiency of Natural Computing Algorithms in DOA Estimation Using a Noise Filtering Approach. Circuits Syst Signal Process 32, 1991–2001 (2013). https://doi.org/10.1007/s00034-012-9538-3

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-012-9538-3

Keywords

Search

Navigation