An RF Direction Finding Payload for UAVs with Deep Learning Direction Prediction Via Resnet

This article describes an RF Direction Finding (DF) payload developed for UAV systems. DF payloads sense RF signals using an antenna array and process the received signals at each antenna location to estimate the number of transmitting RF sources and their bearing relative to the payload. This article uses an open source Software Defined Radio (SDR) known as the KrakenSDR which senses transmitted RF data with (5) antennas. A new deep learning architecture is proposed for estimating the azimuthal Direction of Arrival (DoA) of RF signals from the sensed KrakenSDR antenna data. The recent availability of the compact and comparatively lightweight KrakenSDR hardware for DF applications make academic investigation of this sensor for UAS possible. DF payloads are used in a wide variety of important applications including search-and-rescue, signal intelligence, RF source geolocation, spectrum monitoring, spectrum enforcement and disaster management contexts. This article describes results for a new DoA estimation algorithm and includes discussion on integration challenges, mechanical and electromagnetic design considerations and the payload Size Weight and Power-Cost (SWaP-C) metrics using the KrakenSDR hardware.