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1. Adaptive Beamforming Using Scattering From a Drone Swarm

   https://doi.org/10.1109/WMCS49442.2020.9172335  

   Egarguin, Neil J.; Jackson, David R.; Onofrei, Daniel; Leclerc, Julien; Becker, Aaron (May 2020, 2020 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems (WMCS))

   null (Ed.)

   There may be situations where a direct line of sight between a transmitter and a receiver is blocked. In such a situation it may be possible to transmit a signal upward from a transmitter to a swarm of drones, each of which carries a scattering object. By positioning each drone properly, the scattered signal from the drones can add coherently in a given direction, forming a beam in that direction. The altitude of each drone is used as a degree of freedom in order to change the phase of the signal scattered by the drone. For a given set of horizontal drone positions, the drone altitudes can be determined to produce a main beam in a given direction. The drone positions can also be optimized to focus a beam in a given direction while producing pattern nulls in other prescribed directions with very small sidelobes. 

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2. Direction Estimation in 3D Outdoor Air–Air Wireless Channels through Machine Learning

   https://doi.org/10.3390/s23239524  

   Syed, Muhammad Hashir; Singh, Maninderpal; Camp, Joseph (December 2023, Sensors)

   UAVs need to communicate along three dimensions (3D) with other aerial vehicles, ranging from above to below, and often need to connect to ground stations. However, wireless transmission in 3D space significantly dissipates power, often hindering the range required for these types of links. Directional transmission is one way to efficiently use available wireless channels to achieve the desired range. While multiple-input multiple-output (MIMO) systems can digitally steer the beam through channel matrix manipulation without needing directional awareness, the power resources required for operating multiple radios on a UAV are often logistically challenging. An alternative approach to streamline resources is the use of phased arrays to achieve directionality in the analog domain, but this requires beam sweeping and results in search-time delay. The complexity and search time can increase with the dynamic mobility pattern of the UAVs in aerial networks. However, if the direction of the receiver is known at the transmitter, the search time can be significantly reduced. In this work, multi-antenna channels between two UAVs in A2A links are analyzed, and based on these findings, an efficient machine learning-based method for estimating the direction of a transmitting node using channel estimates of 4 antennas (2 × 2 MIMO) is proposed. The performance of the proposed method is validated and verified through in-field drone-to-drone measurements. Findings indicate that the proposed method can estimate the direction of the transmitter in the A2A link with 86% accuracy. Further, the proposed direction estimation method is deployable for UAV-based massive MIMO systems to select the directional beam without the need to sweep or search for optimal communication performance. 

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3. Wind awareness for energy consumption in drone simulations (demo paper)

   https://doi.org/10.1145/3557915.3561045  

   Kelleher, Noah; Ramirez, Ricardo; Salihovic, Adnan; McKay, Nathan; Kelly, Jonathan; Chen, Hengwei; Trajcevski, Goce (November 2022, 30th International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2022)

   Drone simulators are often used to reduce training costs and prepare operators for various ad-hoc scenarios, as well as to test the quality of algorithmic and communication aspects in collaborative scenarios. An important aspect of drone missions in simulated (as well as real life) environments is the operational lifetime of a given drone, in both solo and collaborative fleet settings. Its importance stems from the fact that the capacity of the on-board batteries in untethered (i.e., free-flying) drones determines the range and/or the length of the trajectory that a drone can travel in the course of its surveilance or delivery missions. Most of the existing simulators incorporate some kind of a consumption model based on different parameters of the drone and its flight trajectory. However, to our knowledge, the existing simulators are not capable of incorporating data obtained from actual physical measurements/observations into the consumption model. In this work, we take a first step towards enabling the (users of) drones simulator to incorporate the speed and direction of the wind into the model and monitor its impact on the battery consumption as the direction of the flight changes relative to the wind. We have also developed a proof-of-concept implementation with DJI Mavic 3 and Parrot ANAFI drones. 

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4. Optimization of the spatial configuration of local defects in phononic crystals for high Q cavity

   https://doi.org/10.3389/fmech.2020.592787  

   Contreras, D.R.; Martínez, M.; Mayorga, M.; Heo, H.; Walker, E.; Neogi, A. (October 2020, Frontiers of Mechanical Engineering)

   null (Ed.)

   Defects can be introduced within a 2-D periodic lattice to realize phononic cavities or phononic crystal (PnC) waveguides at the ultrasonic frequency range. The arrangement of these defects within a PnC lattice results in the modification of the Q factor of the cavity or the waveguide. In this work, cavity defects within a PnC formed using cylindrical stainless steel scatterers in water have been modified to control the propagation and Q factor of acoustic waveguides realized through defect channels. The defects channel based waveguides within the PnC were configured horizontally, vertically, and diagonally along the direction of the propagation of the acoustic waves. Numerical simulations supported by experimental demonstration indicate that the defect-based waveguide's Q factor is improved by over 15 times for the diagonal configuration compared to the horizontal configuration. It also increases due to an increase in the scatterers' radius, varied from 0.7 -0.95 mm. 

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5. Implementation of an Artificial Immune System to Mitigate Cybersecurity Threats in Unmanned Aerial Systems

   https://doi.org/10.1109/ICII.2019.00013  

   Shivers, Meagan; Llanes, Christian; Sherman, Maxwell (November 2019, IEEE International Workshop on Sensing, Communication, and Control for Unmanned Aerial Systems)

   he pervasive operation of customer drones, or small-scale unmanned aerial vehicles (UAVs), has raised serious concerns about their privacy threats to the public. In recent years, privacy invasion events caused by customer drones have been frequently reported. Given such a fact, timely detection of invading drones has become an emerging task. Existing solutions using active radar, video or acoustic sensors are usually too costly (especially for individuals) or exhibit various constraints (e.g., requiring visual line of sight). Recent research on drone detection with passive RF signals provides an opportunity for low-cost deployment of drone detectors on commodity wireless devices. However, the state of the arts in this direction rely on line-of-sight (LOS) RF signals, which makes them only work under very constrained conditions. The support of more common scenarios, i.e., non-line-of-sight (NLOS), is still missing for low-cost solutions. In this paper, we propose a novel detection system for privacy invasion caused by customer drone. Our system is featured with accurate NLOS detection with low-cost hardware (under $50). By exploring and validating the relationship between drone motions and RF signal under the NLOS condition, we find that RF signatures of drones are somewhat “amplified” by multipaths in NLOS. Based on this observation, we design a two-step solution which first classifies received RSS measurements into LOS and NLOS categories; deep learning is then used to extract the signatures and ultimately detect the drones. Our experimental results show that LOS and NLOS signals can be identified at accuracy rates of 98.4% and 96% respectively. Our drone detection rate for NLOS condition is above 97% with a system implemented using Raspberry PI 3 B+. 

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