Application of Artificial Neural Networks for Efficient High-Resolution 2D DOA Estimation

A novel method to provide high-resolution Two-Dimensional Direction of Arrival (2D DOA) estimation employing Artificial Neural Networks (ANNs) is presented in this paper. The observed space is divided into azimuth and elevation sectors. Multilayer Perceptron (MLP) neural networks are employed to detect the presence of a source in a sector while Radial Basis Function (RBF) neural networks are utilized for DOA estimation. It is shown that a number of appropriately trained neural networks can be successfully used for the high-resolution DOA estimation of narrowband sources in both azimuth and elevation. The training time of each smaller network is significantly reduced as different training sets are used for networks in detection and estimation stage. By avoiding the spectral search, the proposed method is suitable for real-time applications as it provides DOA estimates in a matter of seconds. At the same time, it demonstrates the accuracy comparable to that of the super-resolution 2D MUSIC algorithm.