SUMMARYThis 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.

This article presents development and validation of an artificial neural network (ANN)‐based model for accurate two‐dimensional direction of arrival estimation of two coherent sources. The ANN model is developed using simulated signals and its performance is evaluated by measurements inside an anechoic chamber. Simulations and experiments show good agreement in estimating both azimuth and elevation angles of coherent sources. The proposed model demonstrates ability to separate two closely spaced sources and outperforms MUSIC with spatial smoothing preprocessing in the case of small number of signal snapshots. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:2558–2562, 2014