Direction-finding for unmanned aerial vehicles using radio frequency methods

Abstract

Nowadays, drones, also known as unmanned aerial vehicles (UAVs), are used for a wide range of purposes in the civil, industrial, and military domains. The greater the number of UAVs, the more serious the potential security risks correlated to them. To address these risks, it became necessary to develop systems that can detect, locate, and possibly neutralize such an object. The detection can be performed using different sensors such as imaging, radar, acoustic, and radio frequency (RF) sensors. The localization of the target drone, which can be done using direction finding (DF) methods, is an essential part of such a system. Thus, an RF-based DF testbed is designed and experimentally assessed in this paper. The testbed is implemented using software defined radio (SDR) platforms from the Universal Software Radio Peripheral (USRP) family as hardware equipment, and the MUSIC algorithm for processing the signals received on four different phase-coherent RF channels. A DJI Mavic Air UAV is used as target. For assessing the DF estimation performance, the direction obtained from the drone flight record is compared with the one estimated using the proposed DF testbed. Two different scenarios, a static one and a dynamic one, are considered to highlight the effectiveness of the proposed DF testbed across various conditions. The obtained results indicate an average estimation error of 1.15 degrees from the drone's actual position, in the case of the static scenario, and of 1.86 degrees in the case of the dynamic scenario, results which are on parity with or surpassed those of other drone DF systems described in the literature. The paper also outlines the benefits and challenges of the current approach.