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A cascade‐connected neural model for improved 2D DOA estimation of an EM signal
Author(s) -
Stoilkovic Marija,
Stankovic Zoran,
Milovanovic Bratislav
Publication year - 2015
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.2081
Subject(s) - cascade , artificial neural network , computer science , a priori and a posteriori , reliability (semiconductor) , direction of arrival , signal (programming language) , antenna (radio) , algorithm , antenna array , data mining , artificial intelligence , telecommunications , engineering , philosophy , power (physics) , physics , epistemology , quantum mechanics , chemical engineering , programming language
Summary This article proposes a neural network‐based approach to increase accuracy of two‐dimensional direction of arrival (DOA) estimation of an electromagnetic signal. The proposed method combines two neural networks developed using simulated and small amount of empirical data, respectively. The output of the simulation‐based neural network represents approximate information on DOAs. It is then considered as a priori knowledge for the small empirical network that is crucial for obtaining more accurate DOA estimates. The developed cascade‐connected model is validated using real data from a rectangular antenna array. Improvements in terms of accuracy and reliability are obtained and compared with the MUSIC algorithm. Copyright © 2015 John Wiley & Sons, Ltd.