An implementation of a direction-finding antenna for mobile communications using a neural network

Direction-finding systems for radio signals are mostly used in mobile communications and avionics applications for antenna tracking or navigation purposes. In general, such systems require accurate calibration and may be sensitive to noise and external interference. In this paper, we investigate the performance of a neural network-based direction-finding system under such conditions. The proposed topology is a hybrid one, combining a simple RF signal beamformer with a neural network. The training of the neural network is accomplished experimentally with a three-element antenna array by varying the beam's direction and the carrier frequency. The error on the estimated direction of arrival caused by the environment and training limitations are investigated.