Skip to main content

Advertisement

Log in

Combining GA and iterative searching DOA estimation for CDMA signals

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper deals with direction-of-arrival (DOA) estimation based on iterative searching technique for code-division multiple access signals. It has been shown that the iterative searching technique is more likely to converge to a local maximum, causing errors in DOA estimation. In conjunction with a genetic algorithm for selecting initial search angle, we present an efficient approach to achieve the advantages of iterative DOA estimation with fast convergence and more accuracy estimate over existing conventional spectral searching methods. Finally, several computer simulation examples are provided for illustration and comparison.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Liu H (2000) Signal processing applications in CDMA communications. Artech House, Boston

    Google Scholar 

  2. Liberti JC Jr, Rappaport TS (1994) Analytical results for capacity improvements in CDMA. IEEE Trans Veh Technol 43(3):680–690

    Article  Google Scholar 

  3. Caffery JJ, Stuber GL (1998) Overview of radiolocation in CDMA cellular networks. IEEE Commun Mag 36(4):38–45

    Article  Google Scholar 

  4. Chiang CT, Chang AC (2003) DOA estimation in the asynchronous DS-CDMA system. IEEE Trans Antennas Propag 51(1):40–47

    Article  Google Scholar 

  5. Capon J (1969) High-resolution frequency-wavenumber spectrum analysis. Proc IEEE 57(8):1408–1418

    Article  Google Scholar 

  6. Schmidt RO (1986) Multiple emitter location and signal parameter estimation. IEEE Trans Antennas Propag 34(3):276–280

    Article  Google Scholar 

  7. Rao BD, Hari KVS (1989) Performance analysis of root-MUSIC. IEEE Trans Acoust Speech Signal Process 37(12):1939–1949

    Article  Google Scholar 

  8. Holland JH (1962) Outline for a logical theory of adaptive systems. J Assoc Comput Mach 9(3):297–314

    MATH  Google Scholar 

  9. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addition Wesley, Reading

    MATH  Google Scholar 

  10. Sharman KC, McClurkin GD (1989) Genetic algorithms for maximum likelihood parameter estimation. In Proceedings of ICASSP’89, Glasgow, UK, pp 2716–2719

  11. Stoica P, Gershman AB (1999) Maximum-likelihood DOA estimation by data-supported grid search. IEEE Signal Process Lett 6(10):273–275

    Article  Google Scholar 

  12. Er MH, Ng BC (1994) A new approach to robust beamforming in the presence of steering vector errors. IEEE Trans Signal Process 42(7):1826–1829

    Article  Google Scholar 

  13. Zacharias CR, Lemes MR, Dal Pino A (1998) Combinig genetic algorithm and simulated annealing: a molecular geometry optimization study. J Mol Struct 430(1):29–39

    Google Scholar 

  14. Grefensteete JJ (1986) Optimization of control parameters for genetic algorithms. IEEE Trans Syst Man Cybern 16(1):122–128

    Article  Google Scholar 

  15. Liang Y, Zhou C, Wang Z, Lee HP, Lim SP (2001) An equivalent genetic algorithm based on extended strings and its convergence analysis. Inf Sci 138(10):119–138

    Article  MATH  MathSciNet  Google Scholar 

  16. Requena-Pérez ME, Albero-Ortiz A, Monzó-Cabrera J, Díaz-Morcillo A (2006) Combined use of genetic algorithms and gradient descent optimization methods for accurate inverse permittivity measurement. IEEE Trans Microw Theory Tech 54(2):615–624

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ann-Chen Chang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chang, AC., Hung, JC. Combining GA and iterative searching DOA estimation for CDMA signals. Neural Comput & Applic 19, 1003–1011 (2010). https://doi.org/10.1007/s00521-010-0338-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-010-0338-z

Keywords

Navigation