Skip to main content
SpringerLink
Log in

Comparative Analysis of ML-PSO DOA Estimation with Conventional Techniques in Varied Multipath Channel Environment

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In the field of array signal processing, direction of arrival (DOA) estimation is a prime area of research. DOA estimation and adaptive beamforming (ABF) are the main issues in smart antennas, which dynamically find the direction of desired and interfering users and finds the optimum weights for beamforming. There are numerous spectral and eigen structure algorithms for estimating the direction of narrow band sources. However, in a complex multipath channel environment, received signals from different directions are fully or partially correlated, which prevents the applications of high resolution techniques to estimate the direction of incoming signals. Maximum likelihood (ML) is an efficient technique for DOA estimation in a low signal to noise ratio (SNR) and coherent channel environment. In this paper, we use particle swarm optimization (PSO) for estimating ML solution by optimizing complex non linear multimodal function over a high dimensional space in linear arrays. PSO-ML estimator has been compared with conventional DOA estimation techniques in uncorrelated, partially correlated and coherent channel environment. The performance of PSO-ML estimator and conventional algorithms are analyzed in varying partially correlated channel environment. The simulation results demonstrate that PSO based estimator gives superior statistical performance.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Schmidt, R. O. (1986). Multiple emitter location and signal parameter estimation. IEEE Transactions on Antenna Propagation, 34(3), 276–280.

    Article  Google Scholar 

  2. Capon, J. (1969). High-resolution frequency-wavenumber spectrum analysis. Proceedings of IEEE, 57(8), 1408–1414.

    Article  Google Scholar 

  3. Roy, R., & Kailath, T. (1989). ESPRIT-estimation of signal parameters via rotational invariance techniques. IEEE Transactions on Signal Processing, 37(7), 984–995.

    Article  MATH  Google Scholar 

  4. Godara, L. C. (1997). Application of antenna arrays to mobile communications, part II: Beam-forming and direction-of-arrival considerations. Proceedings of IEEE, 85, 1195–1245.

    Article  Google Scholar 

  5. Sharma, A., & Mathur, S. (2016). Performance analysis of adaptive array signal processing algorithms. IETE Technical Review, 33(5), 472–491.

    Article  Google Scholar 

  6. Shan, T. J., Wax, M., & Kailath, T. (1985). On spatial smoothing for direction-of-arrival estimation of coherent signals. IEEE Transactions on Acoustics, Speech, and Signal Processing, 33(4), 806–811.

    Article  Google Scholar 

  7. Pillai, S. U., & Kwon, B. H. (1989). Forward/backward spatial smoothing techniques for coherent signal identification. IEEE Transactions on Acoustics, Speech, and Signal Processing, 7(1), 8–15.

    Article  MATH  Google Scholar 

  8. Sarkar, T. K., & Pereira, O. (1995). Using the matrix pencil method to estimate the parameters of a sum of complex exponentials. IEEE Antennas and Propagation Magazine, 37(1), 48–55.

    Article  Google Scholar 

  9. Yilmazer, N., Koh, J., & Sarkar, T. K. (2006). Utilization of a unitary transformation for efficient computation in the matrix pencil method to find the direction of arrival. IEEE Transactions on Antennas and Propagation, 54(1), 175–181.

    Article  MathSciNet  MATH  Google Scholar 

  10. Gonnouni, A. E., Ramon, M. M., Alvarez, J. L. R., Valls, G. C., Vidal, A. R. F., & Christodoulou, C. G. (2012). A support vector machine MUSIC algorithm. IEEE Transactions on Antennas and Propagation, 60(10), 4901–4910.

    Article  MathSciNet  MATH  Google Scholar 

  11. Zhang, Y. F., & Ye, Z. F. (2008). Efficient method of DOA estimation for uncorrelated and coherent signals. IEEE Antennas Wireless Propagation Letters, 7, 799–802.

    Article  Google Scholar 

  12. Stoica, P., & Sharman, K. C. (1990). Maximum likelihood methods for direction-of-arrival estimation. IEEE Transactions on Acoustics, Speech Signal Processing, 38, 1132–1143.

    Article  MATH  Google Scholar 

  13. Ziskind, I., & Wax, M. (1988). Maximum likelihood localization of multiple sources by alternating projection. IEEE Transactions on Acoustics, Speech, Signal Processing, 36(10), 1553–1560.

    Article  MATH  Google Scholar 

  14. Sharman, K. (1988). Maximum likeliood estimation by simulated annealing (pp. 2741–2744). Proc.: ICASSP.

    Google Scholar 

  15. Stoica, P., & Gershman, A. B. (1999). Maximum-likelihood DOA estimation by data-supported grid search. IEEE Signal Processing Letters, 6, 273–275.

    Article  Google Scholar 

  16. Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: The University of Michigan Press.

    Google Scholar 

  17. Li, M., & Lu, Y. (2007). A refined genetic algorithm for accurate and reliable DOA estimation with a sensor array. Wireless Personal Communications, 43(2), 533–547.

    Article  Google Scholar 

  18. Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, 4, 1942–1948.

    Article  Google Scholar 

  19. Gies, D., & Samii, Y. R. (2003). Particle swarm optimization for reconfigurable phased-differentiated array design. Microwave and Optical Technology Letters, 38, 168–175.

    Article  Google Scholar 

  20. Khodier, M. M., & Christodoulou, C. G. (2005). Linear array geometry synthesis with minimum side lobe level and null control using particle swarm optimization. IEEE Transaction on Antennas and Propagation, 53, 2674–2679.

    Article  Google Scholar 

  21. Robinson, J., & Samii, Y. R. (2004). Particle swarm optimization in electromagnetics. IEEE Transaction on Antennas and Propagation, 52, 397–407.

    Article  MathSciNet  MATH  Google Scholar 

  22. Ciuprina, G., Ioan, D., & Munteanu, I. (2004). Use of intelligent-particle swarm optimization in electromagnetics. IEEE Transaction on Magnetics, 38, 1037–1040.

    Article  Google Scholar 

  23. Messerschmidt, L., & Engelbrecht, A. P. (2004). Learning to play games using a PSO-based competitive learning approach. IEEE Transactions on Evolutionary Computation, 8, 280–288.

    Article  Google Scholar 

  24. Chen, Y., Wang, H., & Su, J. (2006). Particle swarm optimization for image noise cancellation. In Proceedings of the first international conference on innovative computing, information and control (pp. 587–590). Washington DC: IEEE Computer Society.

  25. Li, M., & Lu, Y. (2008). Maximum likelihood DOA estimation in unknown coloured noise fields. IEEE Transaction on Aerospace and Electronics Systems, 44(3), 1942–1948.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics Instrumentation & Control Engineering, University of Petroleum & Energy Studies, Dehradun, 248007, India

    Abhinav Sharma

  2. Department of Electronics & Communication Engineering, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, 263145, India

    Sanjay Mathur

Authors
  1. Abhinav Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanjay Mathur
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abhinav Sharma.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, A., Mathur, S. Comparative Analysis of ML-PSO DOA Estimation with Conventional Techniques in Varied Multipath Channel Environment. Wireless Pers Commun 100, 803–817 (2018). https://doi.org/10.1007/s11277-018-5350-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-018-5350-0

Keywords

Search

Navigation