In this work, we present quasi-monostatic Radar Cross Section measurements of different Unmanned Aerial Vehicles at 26-40 GHz. We study the Radar Cross Section signatures of nine different multi-rotor platforms as well as a single Lithium-ion Polymer battery. These results are useful in the design and testing of radar systems which employ millimeter-wave frequencies for superior drone detection. The data shows how radio waves are scattered by drones of various sizes and what impact the primary construction material has on the received Radar Cross Section signatures. Matching our intuition, the measurements confirm that larger drones made of carbon fiber are easier to detect, whereas drones made from plastic and styrofoam materials are less visible to the radar systems. The measurement results are published as an open database, creating an invaluable reference for engineers working on drone detection.

The millimeter wave bands are being increasingly considered for wireless communication to unmanned aerial vehicles (UAVs). Critical to this undertaking are statistical channel models that describe the distribution of constituent parameters in scenarios of interest. This paper presents a general modeling methodology based on data-training a generative neural network. The proposed generative model has a two-stage structure that first predicts the link state (line-of-sight, non-line-of-sight, or outage), and subsequently feeds this state into a conditional variational autoencoder (VAE) that generates the path losses, delays, and angles of arrival and departure for all the propagation paths. The methodology is demonstrated for 28 GHz air-to-ground channels between UAVs and a cellular system in representative urban environments, with training datasets produced through ray tracing. The demonstration extends to both standard base stations (installed at street level and downtilted) as well as dedicated base stations (mounted on rooftops and uptilted). The proposed approach is able to capture complex statistical relations in the data and it significantly outperforms standard 3GPP models, even after refitting the parameters of those models to the data.

The use of unmanned aerial vehicles (UAVs) for a variety of commercial, civilian, and defense applications has increased many folds in recent years. While UAVs are expected to transform future air operations, there are instances where they can be used for malicious purposes. In this context, the detection, classification, and tracking (DCT) of UAVs (DCT-U) for safety and surveillance of national air space is a challenging task when compared to DCT of manned aerial vehicles. In this survey, we discuss the threats and challenges from malicious UAVs and we subsequently study three radio frequency (RF)-based systems for DCT-U. These RF-based systems include radars, communication systems, and RF analyzers. Radar systems are further divided into conventional and modern radar systems, while communication systems can be used for joint communications and sensing (JC&S) in active mode and act as a source of illumination to passive radars for DCT-U. The limitations of the three RF-based systems are also provided. The survey briefly discusses non-RF systems for DCT-U and their limitations. Future directions based on the lessons learned are provided at the end of the survey.

This work investigates the problem of unmanned aerial vehicle (UAV) recognition using their radar cross section (RCS) signature. The RCS of six commercial UAVs is measured at 15 and 25 GHz in an anechoic chamber for both vertical–vertical (VV) polarization and horizontal–horizontal (HH) polarization. The RCS signatures are used to train 15 different recognition algorithms, each belonging to one of three different categories: statistical learning (SL), machine learning (ML), and deep learning (DL). The study shows that, while the average accuracy of all the algorithms increases with the signal-to-noise ratio (SNR), the ML algorithm achieved better accuracy than the SL and DL algorithms. For example, the classification tree ML achieves an accuracy of 98.66% at 3-dB SNR using the 15-GHz VV-polarized RCS test data from the UAVs. We investigate the recognition accuracy using the Monte Carlo analysis with the aid of boxplots, confusion matrices, and classification plots. On average, the accuracy of the classification tree ML model performed better than the other algorithms, followed by Peter Swerling’s statistical models and the discriminant analysis ML model. In general, the accuracy of the ML and SL algorithms outperformed the DL algorithms (Squeezenet, Googlenet, Nasnet, and Resnet 101) considered in the study. Furthermore, the computational time of each algorithm is analyzed. The study concludes that, while the SL algorithms achieved good recognition accuracy, the computational time was relatively long when compared to the ML and DL algorithms. Also, the study shows that the classification tree achieved the fastest average recognition time of about 0.46 ms.

The use of unmanned aerial vehicles (UAVs) for different applications has increased tremendously during the past decade. The small size, high maneuverability, ability to fly at predetermined coordinates, simple construction, and affordable price have made UAVs a popular choice for diverse aerial applications. However, the small size and the ability to fly close to the terrain make the detection and tracking of UAVs challenging. Similarly, unmanned underwater vehicles (UUVs) have revolutionized underwater operations. UUVs can accomplish numerous tasks that were not possible with manned underwater vehicles. In this survey paper, we provide features and capabilities expected from current and future UAVs and UUVs, and review potential challenges and threats due to use of such UAVs/UUVs. We also overview the countermeasures against such threats, including approaches for the detection, tracking, and classification of UAVs and UUVs.

A measurement setup, including millimeter-wave and ultrawideband transceivers mounted on both a customized unmanned aerial vehicle and a ground station and a measurement procedure for full 3-D wireless propagation analysis, is described in this work. The custom-made developed system represents a flexible solution for the characterization of wireless channels and especially of urban propagation, as the drone might be easily located almost anywhere from ground level to the buildings' rooftop and beyond. The double-directional properties of the channel can be achieved by rotating directive antennas at the link ends. Other possible applications in urban contexts include the study of outdoor-to-indoor penetration, line-of-sight to non-line-of-sight transition, 3-D scattering from buildings, and air-to-ground channel characterization for drone-assisted wireless communications.