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Empirical ANN models for 2D direction of arrival estimation

Empirical Artificial Neural Network (ANN) models are developed for Two-Dimensional Direction of Arrival (2D DOA) estimation of a source signal. For that purpose, experimental data obtained from measurements in an anechoic chamber are utilized. Performance of ANN models are compared to 2D MUSIC algorithm in regard to estimation accuracy and speed of calculations. It is demonstrated that the proposed models outperform MUSIC in cases when small number of snapshots are utilized for DOA estimation and at the same time, are more suitable for real-time implementation.