DocumentCode :
1799934
Title :
Neural network model for efficient localization of a number of mutually arbitrary positioned stochastic EM sources in far-field
Author :
Stankovic, Zoran ; Doncov, Nebojsa ; Milovanovic, Ivan ; Milovanovic, Bratislav
Author_Institution :
Fac. of Electron. Eng., Univ. of Nis, Nis, Serbia
fYear :
2014
fDate :
25-27 Nov. 2014
Firstpage :
41
Lastpage :
44
Abstract :
An efficient direction of arrival (DOA) estimation of multiple electromagnetic sources by using artificial neural network (ANN) approach is presented in the paper. Electromagnetic sources considered here are of stochastic radiation nature, mutually uncorrelated and at arbitrary angular distance. The approach is based on training of the ANN in which the calculation of correlation matrix in the far-field scan area is done by using the Green function and the correlation of antenna elements feed currents used to describe stochastic sources radiation and then mapping this matrix to the space of DOA in angular coordinate. Once successfully trained, the neural network model is capable to perform an accurate DOA estimation within the training boundaries. Presented example verifies the accuracy of the proposed neural network model.
Keywords :
Green´s function methods; antenna feeds; antenna radiation patterns; correlation methods; direction-of-arrival estimation; matrix algebra; neural nets; stochastic processes; DOA estimation; Green function; antenna element feed current correlation; arbitrary angular distance; artificial neural network model; correlation matrix calculation; efficient direction-of-arrival estimation; far-field scan area; mutually arbitrary positioned stochastic electromagnetic sources; mutually uncorrelated; stochastic radiation nature; stochastic sources radiation; Arrays; Azimuth; Correlation; Direction-of-arrival estimation; Estimation; Neural networks; Stochastic processes; Correlation matrix; DOA estimation; neural model; stochastic radiation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Network Applications in Electrical Engineering (NEUREL), 2014 12th Symposium on
Conference_Location :
Belgrade
Print_ISBN :
978-1-4799-5887-0
Type :
conf
DOI :
10.1109/NEUREL.2014.7011455
Filename :
7011455
Link To Document :
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