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     Research Journal of Applied Sciences, Engineering and Technology


3-D Near Field Source Localization by using Hybrid Genetic Algorithm

1Ayesha Khaliq, 2Fawad Zaman, 1Kiran Sultan and 1Ijaz Mansoor Qureshi
1Department of Electrical Engineering, Air University, Islamabad, Pakistan
2Department of Electrical Engineering, IIU, Islamabad, Pakistan
Research Journal of Applied Sciences, Engineering and Technology  2013  23:4464-4469
http://dx.doi.org/10.19026/rjaset.6.3453  |  © The Author(s) 2013
Received: March 01, 2013  |  Accepted: March 27, 2013  |  Published: December 15, 2013

Abstract

In this study, a method based on hybrid Genetic Algorithm is used to jointly estimate 3-D (range, amplitude, elevation angle) parameters of near field sources arriving on uniform linear array. In this approach, Genetic Algorithm is hybridized with pattern Search. In this hybridization, Genetic Algorithm acts as a global search optimizer while Pattern Search is used as a rapid local search optimizer. The performance of Genetic algorithm and pattern search alone is also evaluated. The fitness function used in this study is based on Mean Square Error. This fitness function acted well even in the presence of low signal to noise ratio and requires only single snap-shot to achieve the goal. The validity and efficiency of the proposed approach is checked through a large number of Monte Carlo Simulations.

Keywords:

Direction of arrival, genetic algorithm, near field sources, pattern search, uniform linear array,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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