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International Journal of Antennas and Propagation
Volume 2015, Article ID 542614, 11 pages
Research Article

Efficient DoA Tracking of Variable Number of Moving Stochastic EM Sources in Far-Field Using PNN-MLP Model

1Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, 18 000 Niš, Serbia
2Singidunum University, Danijelova 32, 11000 Belgrade, Serbia

Received 9 August 2015; Accepted 1 December 2015

Academic Editor: Ahmed T. Mobashsher

Copyright © 2015 Zoran Stanković et al. 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.


An efficient neural network-based approach for tracking of variable number of moving electromagnetic (EM) sources in far-field is proposed in the paper. Electromagnetic sources considered here are of stochastic radiation nature, mutually uncorrelated, and at arbitrary angular distance. The neural network model is based on combination of probabilistic neural network (PNN) and the Multilayer Perceptron (MLP) networks and it performs real-time calculations in two stages, determining at first the number of moving sources present in an observed space sector in specific moments in time and then calculating their angular positions in azimuth plane. Once successfully trained, the neural network model is capable of performing an accurate and efficient direction of arrival (DoA) estimation within the training boundaries which is illustrated on the appropriate example.