Linear Vector Quantisation and Uniform Circular Arrays based decoupled two-dimensional angle of arrival estimation

IEEE Radio and Antenna Days of the Indian Ocean

无线电电子学

Ndaw, Joseph D.^1 ; Faye, Andre^1 ; Maïga, Amadou S.^1

Universite Gaston Berger, BP 234, Saint-Louis, Senegal^1

关键词: Artificial neural network approach;    Linear vector quantisation;    Reduced training sets;    Source localisation;    Symmetry properties;    Two-dimensional angles;    Two-dimensional direction of arrivals;    Uniform circular arrays;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/198/1/012006/pdf
DOI  :  10.1088/1757-899X/198/1/012006

来源: IOP

【 摘 要 】

Artificial neural networks (ANN)-based models are efficient ways of source localisation. However very large training sets are needed to precisely estimate two-dimensional Direction of arrival (2D-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 Uniform Circular Arrays (UCA) to build two different datasets for elevation and azimuth angles. Linear Vector Quantisation (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.