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Thanks to recent technological advances, a new generation of low-cost, small, unmanned aerial vehicles (UAVs) is available. Small UAVs, often called drones, are enabling unprecedented applications but, at the same time, new threats are arising linked to their possible misuse (e.g., drug smuggling, terrorist attacks, espionage). In this paper, the main challenges related to the problem of drone identification are discussed, which include detection, possible verification, and classification. An overview of the most relevant technologies is provided, which in modern surveillance systems are composed into a network of spatially-distributed sensors to ensure full coverage of the monitored area. More specifically, the main focus is on the frequency modulated continuous wave (FMCW) radar sensor, which is a key technology also due to its low cost and capability to work at relatively long distances, as well as strong robustness to illumination and weather conditions. This paper provides a review of the existing literature on the most promising approaches adopted in the different phases of the identification process, i.e., detection of the possible presence of drones, target verification, and classification.

Multi-rotor drones, a kind of unmanned equipment which is widely used in the military, commercial consumption and other fields, have been developed very rapidly in recent years. However, their short flight time has hindered the expansion of their application range. This can be addressed by utilizing hydrogen fuel cells, which exhibit high energy density, strong adaptability to ambient temperature, and no pollution emissions, as the power source. Accordingly, the application of hydrogen fuel cells as the power source in multi-rotor drones is a promising technology that has attracted significant research attention. This paper summarizes the development process of hydrogen fuel cell multi-rotor drones and analyzes the key obstacles that need to be addressed for the further development of hydrogen fuel cell multi-rotor drones, including structural light weight, hydrogen storage methods, energy management strategies, thermal management, etc. Additionally, prospects for the future development of hydrogen fuel cell multi-rotor drones are presented.

Growth in application fields of unmanned aerial vehicles (UAVs) and an increase in their total number are followed by higher and higher expectations imposed on improvements in UAV propulsion and energy management systems. Most commercial vertical takeoff and landing (VTOL) UAVs employ a constant pitch propeller that forces a mission execution tradeoff in the majority of cases. An alternative solution, presented here, consists of the use of a variable pitch propeller. The paper summarizes experimental measurements of the propulsion system equipped with an innovative variable pitch rotor. The investigations incorporated characteristics of the rotor for no wind conditions and a new approach to optimize pitch settings in hover flight as a function of UAV weight and energy consumption. As UAV battery capacity is always limited, efficient energy management is the only way to increase UAV mission performance. The study shows that use of a variable pitch propeller can increase the maximal takeoff weight of the aircraft and improve power efficiency in hover, especially if load varies for different missions. The maximal thrust measured was 31% higher with respect to the original blade settings. The coefficient of thrust during hover showed an increase of 2.6% up to 7.5% for various pitch angles with respect to the original fixed propeller.

Rotors play a major role in various applications including ventilation and propulsion systems such as in helicopters, drones, gas turbines and wind turbines. This visualization of instantaneous vortical structures (identified by the k 2 criterion) shows complex flow structures emanating from a twisted drone rotor that is impulsively starting to rotate at 1600 rpm. Initially, a starting vortex is formed as a result of lift generation and shed as a connected vortex tube from the entire surface of the blade, which has a strong connection to the blade tip via the so-called tip vortex. Leading edge separation occurs at span positions of high twist, followed by wave-induced breakdown to turbulence along the whole wing span. This turbulence then sheds as small-scale vortices into the wake and dissipates. Understanding the behaviour of these vortices from such complex blades and how they interact with the other blade is critical to design more efficient and potentially more silent propellers.

Aiming at the difficult problem of the classification between flying bird and rotary-wing drone by radar, a micro-motion feature classification method is proposed in this paper. Using K-band frequency modulated continuous wave (FMCW) radar, data acquisition of five types of rotor drones (SJRC S70 W, DJI Mavic Air 2, DJI Inspire 2, hexacopter, and single-propeller fixed-wing drone) and flying birds is carried out under indoor and outdoor scenes. Then, the feature extraction and parameterization of the corresponding micro-Doppler (m-D) signal are performed using time-frequency (T-F) analysis. In order to increase the number of effective datasets and enhance m-D features, the data augmentation method is designed by setting the amplitude scope displayed in T-F graph and adopting feature fusion of the range-time (modulation periods) graph and T-F graph. A multi-scale convolutional neural network (CNN) is employed and modified, which can extract both the global and local information of the target’s m-D features and reduce the parameter calculation burden. Validation with the measured dataset of different targets using FMCW radar shows that the average correct classification accuracy of drones and flying birds for short and long range experiments of the proposed algorithm is 9.4% and 4.6% higher than the Alexnet- and VGG16-based CNN methods, respectively.

We present a tutorial introduction to the multi-rotor unmanned aerial vehicles, often simply referred as drones. We first explain typical configuration, components and construction of the drones. We then provide basic kinematic and dynamic modeling of drones and their principle of flight. Some representative motion control techniques are then presented, which take into account the issue of under-actuation of the drones. State estimation problem of the drones, that is crucial for their proper flying, yet, should be done only by using onboard sensors and their sensor fusion, is explained. Some emerging research directions requiring capability beyond typical drones are also mentioned.

It is of great significance to detect drones in airspace due to the substantial increase and regrettable misuse in the consumer market. In this paper, we establish a micro-motion theoretical model of a drone and analyze the micro-Doppler signature of rotor targets and the flicker mechanisms of the multi-rotor targets. Hence, for the target recognition problem of multi-rotor drones, a multi-rotor target micro-Doppler parameter estimation method is proposed. Firstly, a signal frequency domain segmentation method is proposed based on the complex variational mode decomposition (CVMD) to separate the high-frequency part of the high-frequency flicker in the frequency domain. Secondly, for the signal after frequency domain segmentation, a flicker time domain position method based on singular value decomposition (SVD) is proposed. Finally, by integrating CVMD frequency domain segmentation and SVD time domain positioning, the reconstruction of multi-rotor target scintillation at different speeds is realized, and the micro-motion parameters of rotor blades are successfully estimated. The simulation results show that the method has high accuracy in estimating the micro-motion parameters of a multi-rotor, which makes up for the shortage of the traditional method in estimating the micro-motion parameters of the multi-rotor target.

Current studies rarely mention radar detection of hybrid vertical take-off and landing (VTOL) fixed-wing drones. We investigated radar signals of an industry-tier VTOL fixed-wing drone, TX25A, compared with the radar detection results of a quad-rotor drone, DJI Phantom 4. We used an X-band pulse-Doppler phased array radar to collect tracking radar data of the two drones in a coastal area near the Yellow Sea in China. The measurements indicate that TX25A had double the values of radar cross-section (RCS) and flying speed and a 2 dB larger signal-to-clutter ratio (SCR) than DJI Phantom 4. The radar signals of both drones had micro-Doppler signals or jet engine modulation (JEM) produced by the lifting rotor blades, but the Doppler modulated by the puller rotor blades of TX25A was undetectable. JEM provides radar signatures such as the rotating rate, modulated by the JEM frequency spacing interval and the number of blades for radar automatic target recognition (ATR), but also interferes with the radar tracking algorithm by suppressing the body Doppler. This work provides an a priori investigation of new VTOL fixed-wing drones and may inspire future research.

The electric propulsion system serves as the power source for multi-rotor drones, helping them complete various maneuvering actions. The reliability of this system directly affects whether the drone can successfully complete its mission. The multi-rotor drone propulsion system is a k-out-of-n system with functional dependence (FDEP). With the insufficient basis for selecting k-values, the problem of incalculable reliability caused by computational space explosion due to voting gates, and the uncertain impact of functional dependence on system reliability, we propose a reliability evaluation method based on controllability theory and BN (Bayesian network) reconstruction. The drone is dynamically modeled, and a control model is built, and k-values are selected through different failure combination controllability evaluations. We model the system with BN, use functional dependent components as BN node inputs, and reconstruct BN via an adder model to solve the problem of exponential growth in the conditional probability table. This paper analyzes system reliability, safety, and the impact of FDEP on the system, and conducts component importance analysis. The result provides important reference for the reliability, safety assessment, and dynamic maintenance processes of multi-rotor drone.

This paper describes a system and technique for controlling the attitude and direction of a quadrotor vertical take-off and landing unmanned aerial vehicle (VTOL UAV) using a horizontal thrust system (horizontal thruster), and hereby we call the system “Thruster Quad-rotor”. The thruster quad-rotor operates by adjusting the rotational speed of each rotor's propellers to hold the quad-rotor’s attitude (angular orientation and linear position in flight), while simultaneously control all direction horizontal motions in the horizontal plane using four thrusters installed on each arm of the quad-rotor. Each rotor is attached to an arm “tube”. At the arm, on the tip, there is a thruster (electric duct fan - EDF). The four arms of the quad rotor are connected to the central body and aligned symmetrically at 45-degree angles to the X and Y axes forming a quad-rotor with “X” configuration. The horizontal thrusters enhance the effectiveness of the quad-rotor with novel design modifications; specifically, the horizontal thrusters increase the typical under-actuation to conventional quad-rotors. The application of the novel thruster concept leads to an increase in maneuvering of the UAV and an ability to resist winds and fly in tight spaces and scenarios, which presents flight’s mission limitations. This paper will include the practicality of this new system, its conceptual and design aspects, and the mathematical modeling of the channels for the new UAV's enhanced control and guidance system.