Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
