Decoupled 2D DOA estimation using LVQ neural networks and UCA arrays

Artificial neural networks (ANN)-based models are efficient ways of source localization. However very large training sets are needed to precisely estimate two-dimension DOA with ANN models. In this paper we present a fast artificial neural network approach for 2D DOA estimation with reduced training sets sizes. We exploit the symmetry properties of UCA arrays to build two different datasets for elevation and azimuth angles. Linear Vector Quantization (LVQ) neural networks are then sequentially trained on each dataset to separately estimate elevation and azimuth angles. A multilevel training process is applied to further reduce the training sets sizes.