Experimental antenna array calibration with artificial neural networks

It is well known that to perform accurate Direction of Arrivals (DOA) estimation using algorithms like MUSIC (MUltiple SIgnals Classification), antenna array data must be calibrated to match the theoretical model upon which DOA algorithms are based. This paper presents experimental measurements from independent sources obtained with a linear antenna array and proposes a novel calibration technique based on artificial neural networks trained with experimental and theoretical steering vectors. In this context, the performance of 3 types of neural networks—ADAptive LInear Neuron (ADALINE) network, Multilayer Perceptrons (MLP) network and Radial Basis Functions (RBF) network—is assessed. This is then compared with other calibration techniques, thus demonstrating that the proposed technique works well while being very simple to implement. The presented results cover operation with a single signal source and with two uncorrelated sources. The proposed method is applicable to arbitrary array topologies, but is presented herein in conjunction with a uniform linear array (ULA).