Search

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. [ABSTRACT FROM PUBLISHER]

Neural networks (NNs) have proven to be a very powerful tool both for one-dimensional (1D) and two-dimensional (2D) direction of arrival (DOA) estimation. By avoiding complex and time-consuming mathematical calculations, NNs estimate DOAs almost instantaneously. This feature makes them very convenient for real-time applications. Further, unlike the well known MUSIC algorithm, neural network-based models provide accurate directions without additional calibration procedure of antenna array and a priori knowledge of the number of sources. In this review paper, the results achieved by the research group at the Faculty of Electronic Engineering in Nis are presented. The problem of DOA estimation of narrowband signals impinging upon different configurations of antenna arrays is addressed. Both Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are considered, and their advantages and disadvantages are discussed. To improve the resolution of DOA estimates, sectorization model is introduced. As shown in this work, neural network-based models demonstrate high-resolution localization capabilities and much better efficiency than the MUSIC. [ABSTRACT FROM PUBLISHER]