The use of amateur drones is expected to significantly increase over the upcoming years. However, regulations do not allow such drones to fly over all areas, in addition to typical altitude limitations. As a result, there is an urgent need for amateur drone surveillance solutions. These solutions should include means of accurate detection, classification, and localization of the unwanted drones in a no-fly zone. In this article, we give an overview of promising techniques for modulation classification and signal-strength-based localization of amateur drones by using surveillance drones. By introducing a generic altitude-dependent propagation model, we show how detection and localization performance depend on the altitude of surveillance drones. Particularly, our simulation results show a 25 dB reduction in the minimum detectable power or 10 times coverage enhancement of a surveillance drone by flying at the optimum altitude. Moreover, for a target no-fly zone, the location estimation error of an amateur drone can be remarkably reduced by optimizing the positions of the surveillance drones. Finally, we conclude the article with a general discussion about the future work and possible challenges in aerial surveillance systems.

Tutorial on UAVs: A Blue Sky View on Wireless Communication

Cellular connectivity for UAV systems is interesting because it promises coverage in beyond visual line of sight scenarios. Inter-cell interference has been shown to be the main limiting factor at high altitudes. Using a realistic 3D simulator model, with real base station locations, this study confirms that UAVs at high altitudes suffer from significant interference, resulting in a worse coverage compared to ground users. When replacing the existing base stations by mmWave cells, our results indicate that ground coverage is decreased to only 90%, while UAVs just above rooftop level have a coverage probability of 100%. However, UAVs at higher altitude still suffer from excessive interference. Beamforming has the potential to improve mmWave link budget and to decrease interference and is for this reason a promising technology for ensuring connectivity to aerial users.

Unmanned Aerial Vehicles (UAVs), a.k.a. drones, are increasingly used for different tasks. With more drones in the sky, the risk of accidents rises, sparking the need for conflict management solutions. Aircraft use a system called Automatic Dependent System-Broadcast (ADS-B) to continuously broadcast their position and speed but this system is not suitable for small drones because of its cost, complexity and capacity limitations. Broadband technologies such as Wi-Fi beacons are more suited for such dense scenarios, and they also offer the benefit of wide availability and low cost. The main challenges for Wi-Fi are (a) the multichannel nature of the technology makes transmitter and receiver coordination difficult, and (b) standard chipsets are not designed for frequent broadcast transmission and reception. In this paper, we propose and analyse a multichannel position broadcast solution that is robust against jamming and achieves a reliable location update within 125 ms. In addition, we implement the protocol on inexpensive embedded Wi-Fi modules and analyse the hardware limitations of such devices. Our conclusions are that, even on the simplest Wi-Fi chipsets, our protocol can be implemented to achieve a realistic location broadcast solution that still perfectly mimics simulation and analytical results on the lab bench and still can achieve approximately 4 message/s throughput at a distance of 900 m on flying UAVs.

Investigation of small-scale fading in indoor peer-to-peer networks based on radio channel measurements shows that Rayleigh or double Rayleigh (DR) fading with or without line-of-sight (LOS) component can occur, depending on the mobility and on the scattering properties of the environment. While in double-mobile scenarios, the predominant fading mechanism is a combination of Rayleigh and DR fading, Rician fading and fading events caused by the combination of a constant term with a DR component are also occasionally observed. In single-mobile scenarios, temporal fading is Rician or Rayleigh distributed. Additionally, measurements reveal that fading statistics evolve significantly over time, even for small-scale motions of the nodes. We model the transitions between the fading states by means of a hidden Markov model, parameterized from our measurements. Eventually, we propose a complete simulation model that generates channel realizations by combining measurement-based first-order statistics with geometry-based second-order statistics for both types of node mobility.