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The Scientific World Journal
Volume 2014 (2014), Article ID 310875, 11 pages
Research Article

5D Parameter Estimation of Near-Field Sources Using Hybrid Evolutionary Computational Techniques

1Department of Electronic Engineering, IIU, H-10, Islamabad 44000, Pakistan
2Electrical Department, Air University, Islamabad 44000, Pakistan

Received 30 August 2013; Accepted 1 December 2013; Published 18 February 2014

Academic Editors: M. F. G. Penedo and J. Tang

Copyright © 2014 Fawad Zaman and Ijaz Mansoor Qureshi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Hybrid evolutionary computational technique is developed to jointly estimate the amplitude, frequency, range, and 2D direction of arrival (elevation and azimuth angles) of near-field sources impinging on centrosymmetric cross array. Specifically, genetic algorithm is used as a global optimizer, whereas pattern search and interior point algorithms are employed as rapid local search optimizers. For this, a new multiobjective fitness function is constructed, which is the combination of mean square error and correlation between the normalized desired and estimated vectors. The performance of the proposed hybrid scheme is compared not only with the individual responses of genetic algorithm, interior point algorithm, and pattern search, but also with the existing traditional techniques. The proposed schemes produced fairly good results in terms of estimation accuracy, convergence rate, and robustness against noise. A large number of Monte-Carlo simulations are carried out to test out the validity and reliability of each scheme.