Three dimensional (3D) radiation pattern of an antenna mounted at a drone can significantly influence the airto-ground (A2G) link quality. Even when a drone transmitter is very close to a ground receiver, if the antenna orientations are not aligned properly, a significant degradation can be observed in the received signal power at the receiver. To characterize such effects for a doughnut-shaped antenna radiation pattern, using an ultra-wideband (UWB) transmitter at the drone and a UWB receiver at the ground, we carry out A2G channel measurements to capture the link quality at the ground receiver for various link distances, drone heights, and antenna orientations. We develop a simple analytical model to approximate the influence of 3D antenna patterns on the received signal strength (RSS), which show reasonable agreement with measurements despite the simplicity of the model and the complicated 3D radiation from the UWB antennas. We also explore how the signal strength can be improved when multiple antennas with different orientations are utilized at transmitter/receiver.Index Terms-3D antenna radiation pattern, antenna gain, drone, unmanned aerial vehicle (UAV), UWB.
Next big commercial applications of drones require to fly the drone beyond the visual line of sight (BVLOS). This inevitable ability to fly BVLOS will also necessitate the ability to keep track of the drone's location, in order to ensure successful completion of the intended service. In this context, we explore the fundamental limits of 3D localization of drones in conjunction with the effects of the 3D antenna radiation patterns. Although localization of drone/unmanned aerial vehicle (UAV) is a well-studied topic in the literature, its relationship to the antenna effects remains mostly unexplored. In this paper, we investigate the impact of antenna radiation pattern on the accuracy of time-difference-of-arrival (TDOA)-based localization of the UAV. Specifically, we consider a scenario where a fixed number of radio-frequency (RF) sensors, placed at some known locations on the ground, collect the TDOA measurements from the signals transmitted from the UAV, and estimate the location of the UAV from these observations. In order to study the impact of the antenna effects on the fundamental limits of the TDOA-based positioning scheme, we develop a simple analytical model to approximate the total antenna gains experienced by an air-to-ground (A2G) link, for various orientations of the antennas. We then derive the Cramer-Rao lower bound for the TDOA based localization scheme, for all three combinations of the transmit and the receive antenna orientations: vertical-vertical (VV), horizontal-horizontal (HH), and vertical-horizontal (VH).
Detection of drones carries critical importance for safely and effectively managing unmanned aerial system traffic in the future. Given the ubiquitous presence of the drones across all kinds of environments in the near future, wide area drone detection and surveillance capability are highly desirable, which require careful planning and design of drone sensing networks. In this paper, we seek to meet this need by using the existing terrestrial radio frequency (RF) networks for passive sensing of drones. To this end we develop an analytical framework that provides the fundamental limits on the network-wide drone detection probability. In particular, we characterize the joint impact of the salient features of the terrestrial RF networks, such as the spatial randomness of the node locations, the directional 3D antenna patterns, and the mixed line of sight/non line of sight (LoS/NLoS) propagation characteristics of the air-to-ground (A2G) channels. Since the strength of the drone signal and the aggregate interference in a sensing network are fundamentally limited by the 3D network geometry and the inherent spatial randomness, we use tools from stochastic geometry to derive the closed-form expressions for the probabilities of detection, false alarm and coverage. This, in turn, demonstrates the impact of the sensor density, beam tilt angle, half power beam width (HPBW) and different degrees of LoS dominance, on the projected detection performance. Our analysis reveals optimal beam tilt angles, and sensor density that maximize the network-wide detection of the drones.
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