Movement Of Unmanned Aerial Vehicles Research Articles

Low‐complexity framework for GNSS jamming and spoofing detection on moving platforms

Performance of global navigation satellite systems (GNSSs) mounted on aerial platforms could be degraded by the presence of jamming or spoofing threats. Detection of jamming and spoofing is essential considering practical applications of satellite navigation in passenger aircrafts, unmanned aerial vehicles (UAVs), helicopters and fighters. Different algorithms and methods have been proposed for detection of these threats; however, their usage has many limitations because of their demanding weight, size and computational complexity, when embedded on aerial systems. In this study, the authors develop a theoretical framework to detect the presence of the threat of UAVs. The idea is based on the fact that, due to the UAV motion, the samples of received signal power from a fixed threat and from a GNSS satellite have different empirical probability density functions. Moreover, by using two antennas (an omnidirectional and a down-tilted-directional), they introduce a new method to distinguish between aerial and ground-based threats. The proposed algorithms have a low-computational burden and can consider the fading loss as well. Simulation results show the superior performance of the proposed methods, in terms of detection and false alarm probability, compared to the existing methods.

Autonomous Navigation of a Solar-Powered UAV for Secure Communication in Urban Environments with Eavesdropping Avoidance

This paper considers the navigation of a solar-powered unmanned aerial vehicle (UAV) for securing the communication with an intended ground node in the presence of eavesdroppers in urban environments. To complete this task, the UAV needs to not only fly safely in the complex urban environment, but also take into account the communication performance with the intended node and eavesdroppers. To this end, we formulate a multi-objective optimization problem to plan the UAV path. This problem jointly considers the maximization of the residual energy of the solar-powered UAV at the end of the mission, the maximization of the time period in which the UAV can securely communicate with the intended node and the minimization of the time to reach the destination. We pay attention to the impact of the buildings in the urban environments, which may block the transmitted signals and also create some shadow region where the UAV cannot harvest energy. A Rapidly-exploring Random Tree (RRT) based path planning scheme is presented. This scheme captures the nonlinear UAV motion model, and is computationally efficient considering the randomness nature. From the generated tree, a set of possible paths can be found. We evaluate the security of the wireless communication, compute the overall energy consumption as well as the harvested amount for each path and calculate the time to complete the flight. Compared to a general RRT scheme, the proposed method enables a large time window for the UAV to securely transmit data.

Multi-UAV cooperative target tracking with bounded noise for connectivity preservation

We investigate cooperative target tracking of multiple unmanned aerial vehicles (UAVs) with a limited communication range. This is an integration of UAV motion control, target state estimation, and network topology control. We first present the communication topology and basic notations for network connectivity, and introduce the distributed Kalman consensus filter. Then, convergence and boundedness of the estimation errors using the filter are analyzed, and potential functions are proposed for communication link maintenance and collision avoidance. By taking stable target tracking into account, a distributed potential function based UAV motion controller is discussed. Since only the estimation of the target state rather than the state itself is available for UAV motion control and UAV motion can also affect the accuracy of state estimation, it is clear that the UAV motion control and target state estimation are coupled. Finally, the stability and convergence properties of the coupled system under bounded noise are analyzed in detail and demonstrated by simulations.

UAV Autonomous Tracking and Landing Based on Deep Reinforcement Learning Strategy.

Unmanned aerial vehicle (UAV) autonomous tracking and landing is playing an increasingly important role in military and civil applications. In particular, machine learning has been successfully introduced to robotics-related tasks. A novel UAV autonomous tracking and landing approach based on a deep reinforcement learning strategy is presented in this paper, with the aim of dealing with the UAV motion control problem in an unpredictable and harsh environment. Instead of building a prior model and inferring the landing actions based on heuristic rules, a model-free method based on a partially observable Markov decision process (POMDP) is proposed. In the POMDP model, the UAV automatically learns the landing maneuver by an end-to-end neural network, which combines the Deep Deterministic Policy Gradients (DDPG) algorithm and heuristic rules. A Modular Open Robots Simulation Engine (MORSE)-based reinforcement learning framework is designed and validated with a continuous UAV tracking and landing task on a randomly moving platform in high sensor noise and intermittent measurements. The simulation results show that when the moving platform is moving in different trajectories, the average landing success rate of the proposed algorithm is about 10% higher than that of the Proportional-Integral-Derivative (PID) method. As an indirect result, a state-of-the-art deep reinforcement learning-based UAV control method is validated, where the UAV can learn the optimal strategy of a continuously autonomous landing and perform properly in a simulation environment.

A Fault-Tolerant Control Method for Unmanned Aerial Vehicle (UAV) Formation with Topological Faults Considered

A fault-tolerant control method for the UAV formation that has topological fault, control surface fault, actuator fault and uncertainty is proposed. Firstly, the formation motion model and the UAV motion model are established. Then based on the topological fault detection method, the topological fault reconstruction and optimization algorithm is proposed to realize the formation topological fault reconstruction and optimization with minimum communication cost and formation reconstruction cost. In designing the backstepping fault-tolerant control method, the actuator's fault identification module and the auxiliary system module are used to estimate and compensate for the faults of the actuator and the control surface respectively so as to realize the stable flight of the UAV formation under the conditions of actuator fault, control surface fault and uncertainty. The simulation results verify the superiority of the fault-tolerant control method for the UAV formation with topological faults.

Airborne LiDAR Assisted Obstacle Recognition and Intrusion Detection Towards Unmanned Aerial Vehicle: Architecture, Modeling and Evaluation

With the rapid development of wireless communication and flight control technologies, the unmanned aerial vehicles (UAVs) have been widely used in multiple application scenarios. A typical scenario is massive crowd management of the multi-millions annual Hajj Pilgrimage to Mecca where UAVs are widely utilized to conduct crowd monitoring by carrying sensory devices. The safe flight of a UAV is crucial for ensuring the successful execution of missions. With the aim to overcome the disadvantage caused by the ground station intrusion detection, the combination of UAV and airborne LiDAR has been widely studied in the field of UAV obstacle recognition. This article studies the UAV network architecture under a common scenario and proposes an obstacle recognition and intrusion detection algorithm for UAV based on an airborne LiDAR (ALORID). First, the preprocessing of the data obtained by a LiDAR, i.e., the coordinate conversion of LiDAR data in combination with UAV motion parameters, is completed. Then, the LiDAR data graph at the current moment is generated by the image noisy point filtering algorithm. After that, the improved density-based spatial clustering of applications with noise (DBSCAN) algorithm is used for image clustering of intrusions to obtain the LiDAR time-domain cumulative graph in a certain detection time. Finally, the motion recognition and location detection of each cluster are completed. The experiment results verify the effectiveness of the proposed algorithm in identifying the moving state of the intrusions.

MECHANISM OF ENSURING SAFE UAV MOVEMENT UNDER THE CONDITIONS OF RADIO ATTACKS

Purpose. The aim of the article is to reveal the mechanism of formation of safe UAV movement in the conditions of radio attacks. Methodology. Scientists from Ternopil National Technical University named after Ivan Pulyuy have consistently developed and studied several mechanisms for the formation of safe movement of unmanned aerial vehicles in radio attacks in order to create a perfect model with which to launch UAVs in areas with high radio attack. As a result of previous work, the mechanism of formation of safe movement of UAVs in the conditions of radio attacks based on methods of increasing the stability of providing information about the route of the unmanned aerial vehicle in the use of REP and air defense systems. Results. The article reveals the mechanism of formation of safe movement of unmanned aerial vehicle in the conditions of radio attacks. Analysis of known solutions in the field of increasing the stability of the control path of unmanned aerial vehicles and electronic suppression demonstrated the relevance of the problem of forming flight routes of unmanned aerial vehicles bypassing opposing enemy areas, taking into account the use of air defense and electronic warfare. The authors emphasize that most drone control tasks are now automated due to their high complexity and versatility. An automated control system operating under the control of a human operator is used as a control factor on an unmanned aircraft. It is emphasized that the main threats to unmanned aerial vehicles in modern conditions are the possibility of their destruction by air defense systems, as well as disruption of the radio communication and control system between the control center and the UAV by electronic suppression. The need for constant tracking of UAV flight by transmitting commands from the launcher is revealed. It is also emphasized the low level of automation of the onboard control system of the unmanned aerial vehicle and the inability to make adequate decisions on information received from onboard sensors in complex situations that require constant monitoring of UAV flight by a human operator. Scientific novelty. For the first time the functional scheme of the UAV recognition mechanism in the conditions of radio attacks is developed and the mechanism of formation of safe movement of the UAV in the conditions of radio attacks which is based on three basic techniques is defined. The first method is the method of clustering the flight zones of an unmanned aerial vehicle according to the degree of control stability. Based on the second method, the authors propose a method of forming the routes of UAV flights, taking into account the location of air defense and electronic warfare. The last link is the method of assessing the stability of providing information about the route of the unmanned aerial vehicle in terms of the use of air defense and electronic warfare. Practical relevance. The results of the work can be implemented in the process of forming the safe movement of UAVs in the conditions of radio attacks. Keywords: unmanned aerial vehicle; air traffic control; space; security; flight.

Optimization of Energy Utilization in Cognitive UAV Systems

Wireless communication with unmanned aerial vehicle (UAV) has attracted much attention due to its flexibility and low-cost in providing wireless connectivity. In this article, we investigate the spectrum sharing between UAV and terrestrial wireless communication. Two scenarios are considered in this work. Scenario 1: the UAV is energy sufficient to transmit the required data. In this case, our objective is to minimize the time that is needed to complete data transmission. Scenario 2: the battery of the UAV is energy insufficient. In this case, the goal becomes maximizing the size of data that can be transmitted. Different scenarios require different placement of the UAV. When the UAV flies towards the primary user (PU), the sensing result will be more accurate and more opportunities can be utilized to transmit data. However, the interference to primary receiver (PR) and the propulsion energy due to UAV's movement will be increased. In this article, the sensing performance, the power allocation and the UAV's position are jointly optimized to satisfy different requirements of the UAV system. To efficiently utilize the licensed spectrum, a hybrid transmission mode is proposed for the UAV system, in which sensing and power control are jointly employed to protect the PR. Then, efficient iterative algorithms are proposed to solve the optimization problems. Simulations results show that proposed designs can significantly reduce the time that is required to complete data transmission in energy sufficient scenario and enhance the size of data that can be transferred in energy insufficient scenario.

Motion-encoded particle swarm optimization for moving target search using UAVs

This paper presents a novel algorithm named the motion-encoded particle swarm optimization (MPSO) for finding a moving target with unmanned aerial vehicles (UAVs). From the Bayesian theory, the search problem can be converted to the optimization of a cost function that represents the probability of detecting the target. Here, the proposed MPSO is developed to solve that problem by encoding the search trajectory as a series of UAV motion paths evolving over the generation of particles in a PSO algorithm. This motion-encoded approach allows for preserving important properties of the swarm including the cognitive and social coherence, and thus resulting in better solutions. Results from extensive simulations with existing methods show that the proposed MPSO improves the detection performance by 24% and time performance by 4.71 times compared to the original PSO, and moreover, also outperforms other state-of-the-art metaheuristic optimization algorithms including the artificial bee colony (ABC), ant colony optimization (ACO), genetic algorithm (GA), differential evolution (DE), and tree-seed algorithm (TSA) in most search scenarios. Experiments have been conducted with real UAVs in searching for a dynamic target in different scenarios to demonstrate MPSO merits in a practical application.

Post-movement stabilization time for the downwash region of a 6-rotor UAV for remote gas monitoring

Unmanned aerial vehicles (UAV) have been used to monitor gas emissions for research projects, though downwash, the airflow produced by the UAV rotors, is potentially capable of artificially altering gas concentration measurements. Anemometers, placed at ten different distances below a 6-rotor UAV, measured air speeds in the downwash region. The collected data was used in combination with UAV rotor speed data to determine the stabilization time of the downwash region after the UAV has returned to a stable hovering position. The stabilization time will determine the amount of time after UAV movement until reliable concentration readings can be obtained within the downwash region. This paper presents stabilization times after vertical upward and rotational UAV movement.

A Novel Nonstationary 6G UAV-to-Ground Wireless Channel Model With 3-D Arbitrary Trajectory Changes

In order to provide reliable and efficient connections between unmanned aerial vehicles (UAVs) and ground stations (GSs), realistic UAV-to-ground channel models are indispensable. In this article, we propose a novel 3-D nonstationary geometry-based stochastic model (GBSM) for UAV-to-ground multiple-input-multiple-output (MIMO) channels. Distinctive UAV-to-ground channel characteristics, such as time-domain nonstationarity, distinctions between different altitudes, spatial consistency, and 3-D arbitrary UAV movement trajectories, are taken into account. By adjusting parameter settings, the proposed channel model framework is sufficiently general to support multiple frequency bands and multiple scenarios, including millimeter wave (mmWave) and massive MIMO configurations. Statistical properties, including power delay profile (PDP), stationary interval, space-time correlation function (STCF), and root-mean-square (RMS) delay spread are derived and analyzed for different frequencies and scenarios. The accuracy of the proposed model is validated by comparing its statistical properties with corresponding available channel measurements. The proposed channel model will provide a fundamental support for the design, performance evaluation, and optimization of future UAV integrated sixth-generation (6G) wireless networks.

Identification of a simplified mathematical model of an unmanned aerial vehicle

The problem of determining the shape and parameters of a mathematical model in the form of a transfer function for the movement of an unmanned aerial vehicle (UAV) in the vertical plane of space is solved. The angle of deviation of the Elevator is considered as the input signal, and the pitch angle of the UAV is considered as the output signal. We use the results of experimental studies of UAV flight, which are considered as known values of input and output signals under specified flight conditions. The measured discrete values of the experimental results are approximated by a fourth-order polynomial based on regression analysis for ease of use in identification. The analytical substantiation of the need to apply the methods of linearization of the mathematical model of UAV movement and the accepted assumptions for obtaining differential equations of UAV movement relative to the center of mass, allowing to synthesize the required transfer function of the corresponding element of the UAV control system. The results of computer modeling confirmed the validity of the synthesized mathematical model obtained on the basis of structural and parametric identification. This approach can be used to obtain simplified mathematical models that are used to solve problems of synthesis and optimization of control systems not only for UAVS, but also for other dynamic objects.

Frequency model of an essentially nonlinear steering drive with a digital microcontroller

When designing a stabilization system for highly maneuverable unmanned aerial vehicles (UAVs), one of the relevant tasks is to study the operation of the steering drive in the frequency band corresponding to the flexural vibrations of the UAV body. To ensure the stability of the UAV stabilization system, quite conflicting requirements may be imposed on the dynamic characteristics of the drive. In particular, the requirement for a sharp suppression of the amplitude-frequency characteristic at the frequency of UAV bending vibrations with minimal phase distortions in the control band of the longitudinal and lateral channels of the stabilization system can significantly complicate the task of researching the stability of the UAV motion control system. The article discusses an electric drive prototype with a digital microcontroller, designed for a highly maneuverable UAV. Adaptive algorithms of the digital controller make it possible to provide the necessary phase delays in the control frequency band and at the same time almost completely suppress the harmonic components of the control signals at the frequencies of the bending vibrations of the UAV body. The algorithms are essentially nonlinear in nature and are based on a change in the gain of the direct circuit of the drive depending on the frequency of the input signal, which greatly complicates the calculation of the transfer function of the steering drive for use in the frequency model of the stabilization system. Generally, the steering drive is described by a linear minimum-phase system, presented as a transfer function of one of the typical blocks of the first or second order, but for the specified steering drive with given dynamic characteristics, this approach is untenable. As a result of the study, a method for obtaining a frequency model of the steering drive is proposed, which is implemented as a non-minimum phase system, the main property of which is the independence of the amplitude-frequency and phase-frequency characteristics. In the process of research, the results obtained on the proposed model are compared with the results of experiments on a drive prototype and its complete non-linear time model. The main advantage of the proposed frequency model is a fairly simple description of the steering drive in the frequency domain, convenient for use as part of the frequency model of the stabilization system in the study of problems of ensuring the stability of UAV flight.

QoE-Driven UAV-Enabled Pseudo-Analog Wireless Video Broadcast: A Joint Optimization of Power and Trajectory

The explosive demands for high quality mobile video services have caused heavy overload to the existing cellular networks. Although the small cell has been proposed to alleviate such a problem, the network operators may not be interested in deploying numerous base stations (BSs) due to expensive infrastructure construction and maintenance. The unmanned aerial vehicles (UAVs) can provide the low-cost and quick deployment, which can support high-quality line-of-sight communications and have become promising mobile BSs. In this paper, we propose a quality-of-experience (QoE)-driven UAV-enabled pseudo-analog wireless video broadcast scheme, which provides mobile video broadcast services for ground users (GUs). Due to limited energy available in UAV, the aim of the proposed scheme is to maximize the minimum peak signal-to-noise ratio (PSNR) of GUs’ video reconstruction quality by jointly optimizing the transmission power allocation strategy and the UAV trajectory. Firstly, the reconstructed video quality at GUs is defined under the constraints of the UAV's total energy and motion mechanism, and the proposed scheme is formulated as a complex non-convex optimization problem. Then, the optimization problem is simplified to obtain a tractable suboptimal solution with the help of the block coordinate descent model and the successive convex approximation model. Finally, the experimental results are presented to show the effectiveness of the proposed scheme. Specifically, the proposed scheme can achieve over 1.6 dB PSNR gains in terms of GUs’ minimum PSNR, compared with the state-of-the-art schemes, e.g., DVB, SoftCast, and SharpCast.

Noncontact cable force estimation with unmanned aerial vehicle and computer vision

AbstractCables and hangers are critical components of long‐span bridges, tension forces of them are needed to be accurately measured for ensuring the safety of bridges. Traditionally, cable tension forces are measured by attached accelerometers or elastomagnetic (EM) sensors, however, applying these sensors into engineering practice are time‐consuming, labor‐intensive, and highly dangerous. To address these problems, an unmanned aerial vehicle (UAV)‐based noncontact cable force estimation method with computer vision technologies was proposed in this article. Basic concept of the proposed method is to use the UAV‐installed camera for capturing vibration images of cables from a certain distance and cable dynamic properties are extracted by analyzing captured images. It includes two aspects: (a) a line segments detector (LSD) was employed for detecting cable edges from captured video and a line matching algorithm was further proposed for extracting dynamic displacements; (b) the frequency difference of adjacent higher modal frequencies identified from relative displacements of the cable was employed for cable force calculation to avoid the difficulty of extracting fundamental frequency from UAV‐captured video. It should be noted that relative displacement herein refers to the difference between displacements of two points on the same cable. Advantages of the proposed method lie in that the proposed LSD and matching algorithm are more robust than traditional correlation‐based algorithm for calculating dynamic displacements of bridge cables and it does not need to adjust predefined parameters (i.e., subset size in correlation‐based algorithms). In addition, the combination of relative displacement and frequency difference‐based cable force estimation has the capability of enhancing the Fourier spectrum magnitude of bridge cables and reducing the effect of UAV motion on extraction of cable vibration frequencies. The effectiveness and robustness of the proposed method was verified by using an experimental inclined cable and field‐testing data of a long‐span suspension bridge. Results show that calculated cable forces with UAV technology have a good agreement with reference values measured by attached accelerometers and fixed camera, demonstrating correctness and robustness of the proposed method for cable force estimation.

Lightweight 3-D Beamforming Design in 5G UAV Broadcasting Communications

Owning the merits of flexible networking, rapid deployment, strong line-of-sight communications and so on, Unmanned Aerial Vehicle (UAV) has been regarded as a promising technology for the Fifth-Generation (5G) wireless network to realise its ambition of ubiquitous connectivity to support the various applications, e.g., broadcasting/multicasting services. However, the unique features of the UAV systems, e.g., highly agile mobility in the three-dimensional space, and the significant size, weight and power constraints, pose new challenges for 5G broadcasting communications. In order to fully harvest the benefits of UAV-aided 5G communications for broadcasting services, a new position based lightweight beamforming technology is proposed in this paper to enhance the capability of signal reception in 5G UAV broadcasting communications. Instead of relying on the channel estimation to form beams, which would cause significant broadband consumption, the position and mobility information of UAV system is exploited to predict UAV movement and form narrow beams to track UAV for signal enhancement and interference cancellation. To flight against the inherent position error, a new position correction method is designed to jointly utilise the DoA information of 5G system and the position information of the UAV system. Comprehensive simulation experiments are conducted based on 3GPP 5G specifications and the results show that the proposed algorithm outperforms the benchmark Zero-Forcing (ZF) without position prediction and position-based beamforming without position correction.

ANFIS controller design based on pigeon-inspired optimization to control an UAV trajectory tracking task

Unmanned aerial vehicles (UAVs) are flying platforms that have become increasingly used in a wide range of applications. However, the most recent research has aimed to improve the quality of UAV control to achieve its mission accurately. This paper proposes a robust and intelligent control method based on adaptive neuro-fuzzy inference system (ANFIS), and pigeon-inspired optimization algorithm (PIO) to govern the behavior of a three-degree of freedom (3-DOF) quadrotor UAV. The quadrotor was chosen due to its simple mechanical structure; nevertheless, these types of UAVs are highly nonlinear. Intelligent control that uses artificial intelligence computing approach such as fuzzy logic is a suitable choice to better control nonlinear systems. The ANFIS controller is proposed to control the movement of UAV to track a given reference trajectory in 2D vertical plane. The PIO is used to obtain the ANFIS optimal parameters with the aim of improving the quality of the controller and therefore, to minimize tracking error. To evaluate the performance of the ANFIS controller tuned by PIO, a comparison between the proposed ANFIS-PIO, ANFIS and proportional–integral–derivative controllers is illustrated, and comparative results demonstrate that the proposed controller is more effective.

Fuzzy swarm trajectory tracking control of unmanned aerial vehicle

Abstract Accurate and precise trajectory tracking is crucial for unmanned aerial vehicles (UAVs) to operate in disturbed environments. This paper presents a novel tracking hybrid controller for a quadrotor UAV that combines the robust adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) algorithm. The ANFIS-PSO controller is implemented to govern the behavior of three degrees of freedom quadrotor UAV. The ANFIS controller allows controlling the movement of UAV to track a given trajectory in a 2D vertical plane. The PSO algorithm provides an automatic adjustment of the ANFIS parameters to reduce tracking error and improve the quality of the controller. The results showed perfect behavior for the control law to control a UAV trajectory tracking task. To show the effectiveness of the intelligent controller, simulation results are given to confirm the advantages of the proposed control method, compared with ANFIS and PID control methods.

Survey of UAV motion planning

Motion planning is a vital module for unmanned aerial vehicles (UAVs), especially in scenarios of autonomous navigation and operation. This survey delivers some recent state-of-the-art UAV motion planning algorithms and related applications. The logic flow of this survey is divided as the path finding, which is the front-end of most motion planning systems, and the trajectory optimisation, which usually serves as the back-end. Motivation, methodology, problem formulation and derivation are given in this survey, in detail. Finally, a section about real-world applications is given, where roles and effectiveness of most popular motion planning methods are revealed.

Mathematical model of mutual movement of unmanned aerial vehicles

Mathematical model of mutual movement of unmanned aerial vehicles