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Range and Direction of Arrival Estimation of Near-Field Sources in Sensor Arrays using Differential Evolution Algorithm

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Yawar Ali Sheikh, Rizwan Ullah, Zhongfu Ye
10.5120/ijca2016909141

Yawar Ali Sheikh, Rizwan Ullah and Zhongfu Ye. Article: Range and Direction of Arrival Estimation of Near-Field Sources in Sensor Arrays using Differential Evolution Algorithm. International Journal of Computer Applications 139(4):16-20, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Yawar Ali Sheikh and Rizwan Ullah and Zhongfu Ye},
	title = {Article: Range and Direction of Arrival Estimation of Near-Field Sources in Sensor Arrays using Differential Evolution Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {139},
	number = {4},
	pages = {16-20},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Many algorithms have been proposed in recent years for source localization in near field. Some are based on subspace methods and some use evolutionary computing techniques. This article presents the performance of Differential Evolution (DE) algorithm for range and DOA estimation of near field narrow band sources, impinging on a uniform linear array (ULA) of passive sensors. Mean square error (MSE) is used as a fitness function because it requires only a single snapshot to converge and perform better even in negative SNR. The main contribution of this work is to explore the effectiveness of DE without hybridization for uniform linear arrays. The results of DE are compared with the results of Genetic Algorithm (GA). The effectiveness of both the algorithms is tested on the basis of a large number of Monte-Carlo simulations and their statistical analysis.

References

  1. Byrne, D., O'Halloran, M., Glavin, M., Jones, E. 2010. Data independent radar beam forming algorithms for breast cancer detection. PIER 107, 331-348.
  2. Nishiura, T., Nakamura. 2003. Talker localization based on the combination of DOA estimation and statistical sound source identification with microphone array. In IEEE Workshop Statistical Signal Processing, 597–600.
  3. Zaman, F., Qureshi, I. M., Naveed, A., Khan, Z. U. 2012. Joint Estimation of Amplitude, Direction of Arrival and Range of Near Field Sources using Memetic Computing. PIER C, 31, 199-213.
  4. Liang, J., Liu, D., Zeng, X., Wang, W., Zhang, J., Chen, H. 2011. Joint (azimuth-elevation-range) estimation of mixed near-field and far-field sources using two-stage separated steering vector-based algorithm. PIER, 113, 17-46.
  5. Kim, J. H., Yang, I. S., Kim, K. M., Oh, W. T. 2000 Passive ranging sonar based on multi-beam towed array. In Proc. IEEE Oceans, 3, 1495-1499.
  6. Raghu, N. C., Sanyogita, S. 1995. Higher-order subspace based algorithms for passive localization of near-field sources. In Proc. Twenty-Ninth Asilomar Conference Signals, Systems and Computers, Pacific Grove, CA, 777–781.
  7. Huang, Y. D., Barkat, M. 1991. Near-field multiple sources localization by passive sensor array. IEEE Trans. Antennas Propag., 39(7): 968–975.
  8. Y. Zhou, D. Feng, “A new subspace method for the estimation of parameters of near field sources”, Journal of Xidian Univ., 2006, 39(5): 41–45.
  9. Diao, M., Miao, S. 2001. New method of parameter matching for 2-D ESPRIT algorithms. Syst. Eng. Electron, 29(8): 1126–1129.
  10. Ziskind, I., Wax, M. 1988. Maximum likelihood localization of multiple sources by alternating projection. IEEE Trans. on Acoust., Speech, Signal Processing, 36, No. 10, 1553-1560.
  11. Abred-Meraim, K., Hua, Y. 1998. 3-D near field source localization using second order statistics. In Conf. Record of the 31st Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA, USA, 1307–1311.
  12. Sheikh, Y. A., Zaman, F., Qureshi, I. M., Atique-ur-Rehman, M. 2012. Amplitude and Direction of Arrival Estimation using Differential Evolution. In International Conference on Emerging Technologies, DOI: 10.1109/ICET.2012.6375456.
  13. F. Zaman, I. M. Qureshi, A. Naveed, Z. U. Khan, “An Application of Artificial Intelligence for the Joint Estimation of Amplitude and Two Dimensional Direction of Arrival of far field sources using 2-L shape array”, International Journal of Antennas and Propagation, 2013, Article ID 593247, 10 pages.
  14. Zaman, F., Qureshi, I. M., Naveed, A., Khan, J. A., Zahoor, R. M. A. 2012. Amplitude and Directional of Arrival Estimation: Comparison between different techniques. Progress in Electromagnetic research-B (PIER-B), 39, 319-335.
  15. F. Zaman, I. M. Qureshi, A. Naveed, Z. U. Khan, “Real Time Direction of Arrival estimation in Noisy Environment using Particle Swarm Optimization with single snapshot”, Research Journal of Engineering and Technology (Maxwell Scientific organization), 2012, 4(13): 1949-1952.
  16. Zaman, F., Khan, J. A., Khan, Z. U., Qureshi, I. M. 2013. An application of hybrid computing to estimate jointly the amplitude and Direction of Arrival with single snapshot. In IEEE 10th IBCAST, 364-368.
  17. Zaman, F., Khan, S. U., Ashraf, K., Qureshi, I. M. 2014. An Application of Hybrid Differential Evolution to 3-D Near field Source localization. In Proceedings of 2014 11th International Bhurban Conference on Applied Sciences & Technology (IBCAST).
  18. Ao, Y., Chi, H. 2009. Experimental Study on Differential Evolution Strategies. In Global Congress on Intelligent Systems, IEEE computer society, DOI 10.1109/GCIS.2009.31.
  19. Maulik, U. 2011. Analysis of gene microarray data in a soft computing framework. Engineering Applications of Artificial Intelligence, Elsevier, Signal Process, 24, 485-490.
  20. R. Storn, K. Price, “Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces”, Journal of Global optimization, 1997, 11: 341-359.
  21. Errastil, B., Escot, D., Poyatos, D., Montiel, I. 2009. Performance analysis of the Particle Swarm Optimization algorithm when applied to direction of arrival estimation. In ICEAA, 447-450.
  22. Addad, B., Amari, S., Lesage, J. 2011. Genetic algorithms for delays evaluation in networked automation systems. Engineering Applications of Artificial Intelligence, Elsevier, 24: 485-490.
  23. Jiankui, Z., Zishu, H., Benyong, L. 2006. Maximum Likelihood DOA Estimation Using Particle Swarm Optimization Algorithm. Proc. IEEE.

Keywords

Differential Evolution, Direction of Arrival, Evolutionary Computing, Near field, Source Localization.