High-speed Unmanned Aerial Vehicle Research Articles

Improved PHY-layer authentication utilizing multi-modal features for mmWave MIMO UAV-enabled systems

This paper exploits multi-modal Physical (PHY)-layer features in terms of artificial fingerprint, In-phase/Quadrature (IQ) imbalance and Angle of Arrival (AoA) to propose a novel PHY-layer authentication framework for a Millimeter Wave (mmWave) Multiple-Input Multiple-Output (MIMO) Unmanned Aerial Vehicle (UAV)-enabled communication system. First, we resort to the AoA-based spatial fingerprint to effectively address the challenge of channel fingerprint instability induced by high-speed UAV mobility. To further enhance the low discriminability of hardware fingerprints caused by refined manufacturing techniques, artificial Gaussian noise is injected into the transmission signals to assist the receiver in better distinguishing between legitimate and illegitimate UAVs. Then, we jointly combine with inherent IQ imbalance and AoA features to design a hybrid authentication scheme and thus construct a multi-dimensional fingerprint space for a comprehensive characterization of UAV identities. To theoretically evaluate the effectiveness of the proposed authentication framework, the analytical closed-form expressions of performance metrics like false alarm and detection probabilities are also exactly derived based on the statistical signal processing technology and composite hypothesis testing. Finally, we provide large simulation results to validate the correctness and feasibility of the proposed theoretical models, and also discuss the relation between system security and communication service quality under different artificial fingerprint level.

An improved whale optimization algorithm for UAV swarm trajectory planning

For the problem of trajectory planning in the small-scale unmanned aerial vehicle (UAV) swarms, classical intelligent algorithms and newly emerged bio-inspired algorithms often suffer from being trapped in local optima, leading to suboptimal solutions. Moreover, these algorithms fail to ensure optimal solutions and convergence speed in high-speed dynamic UAV networking scenarios. In response to these challenges, this paper proposes an Improved Whale Optimization Algorithm (IWOA) that considers the global perspective. To address the issue of initial solution diversity, an opposition-based learning method is introduced by constructing a reverse population, enhancing the diversity of the initial population. To overcome the problem of the algorithm getting trapped in local optima, random convergence factors, and end-point random neighborhood perturbations are incorporated to accelerate the global convergence speed of the IWOA and prevent it from getting stuck in local optima. Additionally, a grid digital elevation model is employed when constructing the experimental environment model, considering various physical constraints of the UAV swarm. This ensures that the simulation validation of the IWOA algorithm is closer to real-world scenarios. Simulation results indicate that, compared to commonly used algorithms, the IWOA can generate more optimal trajectories for drone swarm planning under constraints resembling real-world scenarios. It exhibits better performance in trajectory evaluation, convergence speed, and stability, thereby meeting the requirements of trajectory planning for small-scale UAV swarms. The proposed IWOA enhances UAV swarm coordination and efficiency, significantly impacting real-world applications in various fields.

A Practical Approach for Estimating Drag Increase due to Volume Increase in the Center Body of a High-Speed UAV

High-speed unmanned aerial vehicles are used for many purposes in aviation. High-speed aircraft do not only fly at supersonic speed, but their subsonic flight performance is also important. Aircraft design is an iterative process in which many disciplines work together. The design process is updated by the negotiation of different disciplines. There may be a demand for a body volume increase in the interior design process. The increase in volume can be achieved by the elongation or expansion of the body. A volume increase in the center body causes an increase in drag. The aim of this study is to predict the effect of the elongation or expansion of the center body on drag in a practical way. It is investigated using a structure proposed with MATLAB and DATCOM. The results in both subsonic and supersonic regimes are formalized separately and compared. It is shown that in case of providing the same volume increase in both subsonic and supersonic regimes, the elongation of the aircraft center body causes less drag compared to widening.

A Deep Reinforcement Learning-Based Intelligent Maneuvering Strategy for the High-Speed UAV Pursuit-Evasion Game

Given the rapid advancements in kinetic pursuit technology, this paper introduces an innovative maneuvering strategy, denoted as LSRC-TD3, which integrates line-of-sight (LOS) angle rate correction with deep reinforcement learning (DRL) for high-speed unmanned aerial vehicle (UAV) pursuit–evasion (PE) game scenarios, with the aim of effectively evading high-speed and high-dynamic pursuers. In the challenging situations of the game, where both speed and maximum available overload are at a disadvantage, the playing field of UAVs is severely compressed, and the difficulty of evasion is significantly increased, placing higher demands on the strategy and timing of maneuvering to change orbit. While considering evasion, trajectory constraint, and energy consumption, we formulated the reward function by combining “terminal” and “process” rewards, as well as “strong” and “weak” incentive guidance to reduce pre-exploration difficulty and accelerate convergence of the game network. Additionally, this paper presents a correction factor for LOS angle rate into the double-delay deterministic gradient strategy (TD3), thereby enhancing the sensitivity of high-speed UAVs to changes in LOS rate, as well as the accuracy of evasion timing, which improves the effectiveness and adaptive capability of the intelligent maneuvering strategy. The Monte Carlo simulation results demonstrate that the proposed method achieves a high level of evasion performance—integrating energy optimization with the requisite miss distance for high-speed UAVs—and accomplishes efficient evasion under highly challenging PE game scenarios.

A High-gain omnidirectional conformal antenna on the aerial vehicle

Abstract A series-fed conformal microstrip antenna with the tail wing of a UAV(Unmanned Aerial Vehicle)is proposed and implemented in this paper. In the target frequency band, the antenna has a nice performance of omnidirectional radiation and high gain, which easily conforms with the tail wing structure, achieving good wind resistance characteristics and meeting the strict aerodynamic requirements of the UAV. The antenna radiator is etched onto a printed board with a dielectric constant of 2.2 and a size of 644mm×112mm×4mm. In addition, the radiator is fixed with PMI form and formed a tail shape with surrounding glass fiber cloth, which has strong rigidity as a whole and has passed the flight test. The test results show that the antenna achieves a gain of more than 4.2 dBi at 564-676 MHz, a gain of more than 5.6 dBi at 600-650 MHz, and a gain of less than 3dB at 564-676MHz. This proposed antenna has a reasonable structure and good performance, which is verified by engineering experiments and is suitable for harsh environment requirements. It is expected that this antenna has wide applications in high-speed UAV communication.

Penetration Strategy for High-Speed Unmanned Aerial Vehicles: A Memory-Based Deep Reinforcement Learning Approach

With the development and strengthening of interception measures, the traditional penetration methods of high-speed unmanned aerial vehicles (UAVs) are no longer able to meet the penetration requirements in diversified and complex combat scenarios. Due to the advancement of Artificial Intelligence technology in recent years, intelligent penetration methods have gradually become promising solutions. In this paper, a penetration strategy for high-speed UAVs based on improved Deep Reinforcement Learning (DRL) is proposed, in which Long Short-Term Memory (LSTM) networks are incorporated into a classical Soft Actor–Critic (SAC) algorithm. A three-dimensional (3D) planar engagement scenario of a high-speed UAV facing two interceptors with strong maneuverability is constructed. According to the proposed LSTM-SAC approach, the reward function is designed based on the criteria for successful penetration, taking into account energy and flight range constraints. Then, an intelligent penetration strategy is obtained by extensive training, which utilizes the motion states of both sides to make decisions and generate the penetration overload commands for the high-speed UAV. The simulation results show that compared with the classical SAC algorithm, the proposed algorithm has a training efficiency improvement of 75.56% training episode reduction. Meanwhile, the LSTM-SAC approach achieves a successful penetration rate of more than 90% in hypothetical complex scenarios, with a 40% average increase compared with the conventional programmed penetration methods.

A Novel Optimized Link-State Routing Scheme with Greedy and Perimeter Forwarding Capability in Flying Ad Hoc Networks

A flying ad hoc network (FANET) is formed from a swarm of drones also known as unmanned aerial vehicles (UAVs) and is currently a popular research subject because of its ability to carry out complicated missions. However, the specific features of UAVs such as mobility, restricted energy, and dynamic topology have led to vital challenges for making reliable communications between drones, especially when designing routing methods. In this paper, a novel optimized link-state routing scheme with a greedy and perimeter forwarding capability called OLSR+GPSR is proposed in flying ad hoc networks. In OLSR+GPSR, optimized link-state routing (OLSR) and greedy perimeter stateless routing (GPSR) are merged together. The proposed method employs a fuzzy system to regulate the broadcast period of hello messages based on two inputs, namely the velocity of UAVs and position prediction error so that high-speed UAVs have a shorter hello broadcast period than low-speed UAVs. In OLSR+GPSR, unlike OLSR, MPR nodes are determined based on several metrics, especially neighbor degree, node stability (based on velocity, direction, and distance), the occupied buffer capacity, and residual energy. In the last step, the proposed method deletes two phases in OLSR, i.e., the TC message dissemination and the calculation of all routing paths to reduce routing overhead. Finally, OLSR+GPSR is run on an NS3 simulator, and its performance is evaluated in terms of delay, packet delivery ratio, throughput, and overhead in comparison with Gangopadhyay et al., P-OLSR, and OLSR-ETX. This evaluation shows the superiority of OLSR+GPSR.

Optimization of high-speed fixed-wing UAV penetration strategy based on deep reinforcement learning

The penetration decision of unmanned aerial vehicles (UAVs) is one of the key components for UAVs to detect or strike crucial targets in adversarial environments. Presently, most methods lack research on penetration decisions under unknown interception information. The executing UAV for decision-making is generally a simplified three-degree-of-freedom model. The decision format is often simplified to path point optimization, lacking control over the stability of complex model attitudes and exhibiting relatively poor robustness. This paper proposes a penetration relative motion theory and aircraft control method based on the six-degrees-of-freedom UAV model to reduce response errors between algorithm control decisions and actual flight control. Using a Markov decision process, a UAV penetration decision control method based on an attitude-overload control loop is designed. This method combines reinforcement learning techniques to enable autonomous penetration decision-making and control for UAVs facing active defense scenarios, enhancing the autonomy, accuracy, and generalization of UAV online decision-making. The paper concludes by implementing autonomous decision-making and control for UAV penetration through the integration of interceptor trajectory prediction with proximal policy optimization. This method reduces the dependency of penetration strategies on interceptor information, ensuring both the generalization ability of penetration strategy models under uncertain information and the consistency of decision stability. Through simulations and experiments, we verified that the penetration strategy based on the pre-sampling PPO method improves the penetration effect by 15.44 % on average and the hit rate by 18.48 %. Moreover, the probability of penetration in different scenarios exceeds 70 %, and the penetration effect is improved by more than 10 % in the case of multiple interceptors. This paper also analyzes the impact of interceptor spatial distribution and the quantity of interceptors on the effectiveness of UAV penetration decision-making.

A review of the problem of modeling the aerodynamics of small-sized ekranoplanes

A characteristic trend in the development of modern transport systems is the widespread introduction of unmanned technologies. One of the new vehicles that are promising for use as high-speed unmanned aerial vehicles are ekranoplanes (WIG crafts). Such devices use the principle of dynamic support above the surface. Therefore, the processes of aerodynamics are decisive for their reliable and effective operation. The presented work considers the problem of modeling aerodynamic processes during the movement of the vehicle near the surface. In contrast to studies in an unrestricted flow, the presence of a surface leads to a number of features that determine the need for special approaches to both physical and mathematical experiments. It is established that each of the methods can ensure the similarity of the modeled processes to the real ones within the defining limits. A brief review of methods of numerical modeling of ekranoplan aerodynamics is presented. The approaches, which based on the Navier-Stokes equations and method of discrete singularities, are considered. There are examples of calculation of the processes of wing aerodynamics herein. The advantages and disadvantages of viscous and inviscid models for research are shown.

Frequency-Offset Information Aided Self Time Synchronization Scheme for High-Dynamic Multi-UAV Networks

Due to the unique merits of unmanned aerial vehicle (UAV) systems, they have already been harnessed for military, public, and civil applications. Time synchronization is a significant premise of formatting and applying UAV networks. However, the irregular high-speed mobile UAVs pose new challenges to time synchronization, especially when external time references are unavailable in some rigid scenarios. Therefore, in these harsh cases, self-time-synchronization (STS) without any external assistance should be concerned to overcome the relative velocity between UAVs caused by irregular high-speed motion. In this paper, a realistic timestamps model for the multi-UAV networks is established, and then a dynamic topology-based maximum likelihood estimator will be developed to carry out the STS. Furthermore, by introducing the information on frequency offset, a new estimator with the closed-form expression is proposed based on a two-way message exchange framework. After that, a tracking algorithm with the assistance of estimation results will be introduced to compensate for the time-varying change of the clock parameters for the dynamic topology UAV networks. To evaluate the performance of the estimator, both the estimation error and Cramér-Rao lower bound are analyzed. Numerical results show that the proposed algorithm exhibits its superiority in STS performance and computational complexity compared to the existing two-way message exchange algorithm using timestamps only.

Adaptive Beam Alignment and Optimization for IRS-Aided High-Speed UAV Communications

In the unmanned aerial vehicle (UAV) enabled wireless communication system, the rapid channel change caused by high-speed UAV may cause the beam misalignment problem. It is a challenge to fast establish and maintain the communications link between UAV and ground base stations. To address this challenge, an IRS-aided high-speed UAV adaptive beam alignment (IRS-UAV-ABA) scheme utilizing double-layer long short-term memory (DL-LSTM) spatial angle prediction model is proposed to improve the effectiveness and reliability of the UAV communication. Firstly, DL-LSTM enabled spatial angle prediction is modeled to facilitate the calculation of the beam coverage adapted to the UAV real-time flight trajectory. Secondly, the non-convex optimization problem for maximizing the received signal-to-noise ratio of moving UAV is formulated to obtain the suboptimal solutions of the BS’ beamforming vector and the IRS’ phase shift matrix. Finally, with the aforementioned beamforming vectors, the phase shift matrix and the adaptive beam coverage, the adaptive dual-beams alignment from BS to UAV and from IRS to UAV can be achieved by the proposed DL-LSTM-enabled IRS-UAV-ABA algorithm. Simulation results verify that the proposed scheme not only can effectively avoid the beam misalignment caused by the mobility and perturbation of UAV, but also has superior spectrum efficiency and stable beamforming gain.

Аналіз можливості застосування комерційних мікротурбореактивних двигунів для високошвидкісних малорозмірних БПЛА оперативно-тактичного рівня

Despite the technological advances that made it possible to create serial commercial micro-turbojet engines with a thrust of up to 1.0 kN, engines of this type in the class of high-speed unmanned aerial vehicles (UAVs) weighing up to 200 kg have not yet received wide distribution. Nevertheless, the use of micro-turbojet engines continues to be considered as promising, and a large amount of work has been devoted to the study of these engines. However, existing studies often show a simplistic approach when a micro-turbojet is considered in isolation from its possible aviation application. The object of this study is micro turbojet engines for high-speed UAVs. The purpose of this study was to evaluate the effectiveness of the use of serial commercial micro turbine engines on high-speed UAVs. The task: to select a gas-dynamic calculation program, perform a mathematical modeling of the characteristics of a micro-turbojet engine, and analyze the conditions and effectiveness of the application of the considered type of engine on high-speed UAVs. Method of the study. The GasTurb14 standard program for gas-dynamic calculation of gas turbine engines was used, with the help of which gas-dynamic calculations were carried out, a structural diagram was obtained, and mathematical modeling of micro-turbojet characteristics was performed. The results. Possible manufacturers and models of engines produced by them and their equipment were identified. It was found that for the considering UAV class, the engine should have a pressure ratio in compressor in the range of 4.2-4.7, and it is advisable to choose the flight operating mode of the micro-turbojet engine at a speed of 92-95% of the maximum. It has also been determined that UAVs with micro-turbojet engines, in comparison with piston engines, easily provide the same flight range with the same fuel relative mass due to at least three times higher speed. Conclusions. The use of a micro-turbojet turns out to be more effective at a flight range of over 300 km, and at a flight speed of more than 150 m/s (540 km/h), a micro-turbojet engine provides a significant advantage over a piston engine for a number of operational and tactical tasks.

Functioning models and comparative analysis of airframe cooling systems of a high-speed unmanned aerial vehicle

Functioning models and comparative analysis of airframe cooling systems of a high-speed unmanned aerial vehicle

Trajectory planning for OTFS-based UAV communications

Unmanned aerial vehicles (UAVs) have attracted growing research interests in recent years, which can be used as cost-effective aerial platforms to transmit collected data packets to ground access points (APs). Thus, it is crucial to investigate robust air-tos-ground (A2G) wireless links for high-speed UAVs. However, the A2G wireless link is unstable as it suffers from large path-loss and severe Doppler effect due to the high mobility of UAVs. In order to meet these challenges, we propose an orthogonal time frequency space (OTFS)-based UAV communication system to relief the Doppler effect. Besides, considering that the energy of UAV is limited, we optimize the trajectory planning of UAV to minimize the energy consumption under the constraints of bit error rate (BER) and transmission rate, where the Doppler compensation is taken into account. Simulation results show that the performance of OTFS-based UAV system is superior to orthogonal frequency division multiplexing (OFDM)-based UAV systems, which can accomplish transmission tasks over shorter distances with lower energy consumption.

Modeling and Control of a Fixed-Wing High-Speed UAV

High-speed Unmanned Aerial Vehicles (UAVs) will be an interesting subject of study in today’s aviation technology because of their ingenuity in obtaining high speeds while maintaining good maneuverability. In this study, modeling and control of a fixed-wing high-speed mini-UAV are performed. Aerodynamic analyses of the vehicle with a wingspan of 1.2 meters and a total take-off weight of 1.1 kg are done with the help of some computational fluid dynamics software. A developed MATLAB/Simulink code evaluates flight performance after a doublet control surface disturbance with six-degrees-of-freedom flight simulations in both longitudinal and lateral directions by a developed MATLAB/Simulink code. The transfer functions are obtained by trimming the aircraft at wing-level for a speed of 155 km/h, and the maximum speed that the mini-UAV could reach is calculated as 400 km/h. Two kinds of different linear controllers are designed to hold the pitch angle of the vehicle to the desired value. The time responses of the controllers are represented, and the elevator deflection effort is evaluated. Finally, a compulsive pitch angle is wanted to be tracked by the two controllers, and their responses are compared in terms of performance and stability.

Intelligent Guidance and Control Methods for Missile Swarm.

High-speed unmanned aerial vehicles (UAVs) are more and more widely used in both military and civil fields at present, especially the missile swarm attack, and will play an irreplaceable key role in the future war as a special combat mode. This study summarizes the guidance and control methods of missile swarm attack operation. First, the traditional design ideas of the guidance and control system are introduced; then, the typical swarm attack guidance and control methods are analyzed by taking their respective characteristics into considering, and the limitations of the traditional design methods are given. On this basis, the study focuses on the advantages of intelligent integrated guidance and control design over traditional design ideas, summarizes the commonly used integrated guidance and control design methods and their applications, and explores the cooperative attack strategy of missile swarm suitable for the integrated guidance and control system. Finally, the challenges of missile swarm guidance and control are described, and the problems worthy of further research in the future are prospected. Summarizing the guidance and control methods of missile will contribute to the innovative research in this field, so as to promote the rapid development of unmanned swarm attack technology.

Beam Alignment for High-Speed UAV via Angle Prediction and Adaptive Beam Coverage

In unmanned aerial vehicle (UAV) communications, continuing beamforming along with the movement of the UAV to mitigate the severe path loss is indispensable, which is called beam tracking. The conventional beam tracking studies focus on tracking the angle resulting from the movement of a target and obtaining the maximum beamforming gain with a narrow beam through phased array-based beamforming. However, the high mobility and perturbation of UAV impose a challenge on aligning narrow beams between the base node and the UAV node in the aerial network. In particular, the spatial angular velocity is significantly high in the field of view (FoV) of antenna array. In this article, we propose a scheme that predicts the spatial angle of the moving object in the next time unit <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¹</xref> and design a wide beam pattern based on the predicted value to reduce the beam misalignment issue. The proposed scheme can establish a robust communication link with the reduced packet loss, by mitigating the variations resulting from angle transition during a time unit, prediction errors, and the angle process noise. Especially, Gaussian process regression (GPR)-based spatial angle prediction method can predict spatial angle for the next time unit, with lower overhead than conventional methods. Considering the fact that UAVs typically travel at speeds from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{40}$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{160}$</tex-math></inline-formula> [km/hr], we verify the simulation results in high-speed scenario such as from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{170}$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{200}$</tex-math></inline-formula> [km/hr]. The simulation results show that the proposed scheme reduces the alignment error while maintaining sufficient signal-to-noise ratio (SNR) condition required for vehicular communication.

Low-cost MIMU based AMS of highly dynamic fixed-wing UAV by maneuvering acceleration compensation and AMCF

Attitude Measurement System (AMS) that constructed by the low-cost Micro-Inertial Measurement Unit (MIMU) is usually adopted as the backup equipment for the fixed-wing Unmanned Aerial Vehicle (UAV) to enhance its safety performance during flight. However, both the large-maneuvering acceleration and the high-frequency propeller dithering are major reasons that decreasing its real-time measurement precision. In addition, there are only tri-axial gyroscope and tri-axial accelerometer measurements could be utilized to compensate the measurement errors in the autonomous MIMU based AMS. In this paper, an Adaptive Mahony Complementary Filter (AMCF) with dynamic tuning of the complementary filter Proportion and Integral (PI) parameters is used to estimate the real-time attitude of the highly dynamic aviation UAV with low-cost MIMU in high-frequency propeller dithering. Meanwhile, the maneuvering acceleration compensation technology is also integrated with the AMCF to eliminate the UAV highly dynamic maneuvering acceleration. Moreover, the attitude of AMS is updated by the quaternion updating algorithm to improve its real-time performance and reliability. Finally, both UAV ground high-speed taxiing experiment and UAV flight experiment are conducted to verify the measurement accuracy of low-cost AMS. The real-time flight experimental results demonstrated that the Rooted Mean Square Error (RMSE) of AMS based on the low-cost MIMU do not exceed 0.882° in terms of pitch angle and 0.864° in roll angle under various maneuvering conditions. These results provide powerful supports for both the UAV developers and the low-cost MIMU based AMS.

Estimation of the Required Dimension of Net to Capture Drone

A method for estimating of the required dimension net to capture drone is proposed. The results of calculations of the drone movement trajectories in horizontal and vertical planes depending on velocity and normal overload are presented. The calculation of the trajectories in the horizontal plane was carried out based on formula of the coordinated turn at constant values of a speed and normal overload. An analytical solution of the differential equation system for the isolated movement of the drone in the vertical plane is obtained. According to performed calculations, delivery of the net to the unwanted drone should be carried out with high-speed unmanned aerial vehicle in order to reduce the interception time and, accordingly, to reduce the required area of net for it capture.

Design and Stability Analysis of High-Speed Unmanned Aerial Vehicle Electric Anti-Skid Braking System

A new combined electric anti-skid braking system of a high-speed unmanned aerial vehicle is designed to improve the stability and to reduce the system sensor noise. An electric anti-skid braking mechanism model considering the sensor noise effect is built in MATLAB/Simulink. The stability of a slip ratio braking system and a deceleration braking system is analyzed using the Routh criterion and Lyapunov stability method. Then the UAV ground taxiing dynamic model is built in LMS Virtual. Lab Motion. The braking performance and dynamic responses are studied under the control of the designed braking system using a co-simulation method. Conclusions show that the designed braking control system can solve the sensor noise problem effectively and the stability and robustness are both ensured under this control law.