Position Of Unmanned Aerial Vehicle Research Articles

Joint Optimization of Spectrum Resource Management and Position Placement for UAV Base Station Networks

The unmanned aerial vehicle (UAV) base station plays a significant role in enhancing the terrestrial network, when the ground base station (GBS) is destroyed in emergent cases or its load exceeds the capacity of the terrestrial network. Presently, many papers focus on optimizing the UAV position deployment and user access, while ignoring the optimization about the spectrum resource management. To solve this problem, we formulate a joint optimization problem of the spectrum resource management and the position placement for UAVs with the constraint of the limited backhaul capacity. Later, the joint optimization problem is modeled as a hierarchical game decision architecture comprised of a UAV position placement game and a spectrum resource management game. Further, we analyze the equilibrium property of the two games and propose two best response- (BR) based optimization algorithms to reach the Nash Equilibriums (NEs) of the two games, respectively. Specifically, the proposed algorithm about the UAV deployment considers the variable granularity local exploration and global random exploration. Simulation results show that the proposed UAV deployment algorithm can improve the total throughput by 7% and 20% at least in comparison with the K-means deployment algorithm and the fixed granularity exploration algorithm, respectively.

Template matching based on convolution neural network for UAV visual localization

Currently, unmanned aerial vehicle (UAVs) are used in many fields, among which UAV localization is the key to UAV autonomous flight capability, and how to locate UAVs in the absence of GNSS signals is a difficult task. In recent years, deep learning has developed rapidly, among which convolutional neural networks are widely used in visual images. In this paper, we apply the convolutional neural network DenseNet to the task of matching between UAV images and satellite remote sensing images for visual localization of UAVs. We make some improvements to address the problems in practice. We propose a quality-aware template matching method based on adaptive adjustment of convolutional feature weights to enhance the feature extraction capability of the model, and introduce a fusion mechanism of multi-scale feature layers. The feature maps of UAV images and satellite images are quality scored so as to locate the position of UAV in satellite images. Qualitative and quantitative experiments are conducted, and the results show the effectiveness and superiority of the method.

Reinforcement Learning Based Dual-UAV Trajectory Optimization for Secure Communication

Unmanned aerial vehicles (UAV) can serve as aerial base stations for users due to their flexibility, low cost, and other characteristics. However, due to the high flight position of UAVs, the air-to-ground (ATG) channels usually dominate with line-of-sight (LoS), which can be easily eavesdropped by multiple eavesdroppers. This poses a challenge to secure communication between UAVs and ground users. In this paper, we study a UAV-aided secure communication in an urban scenario where a legitimate UAV Alice transmits confidential information to a legitimate user Bob on the ground in the presence of several eavesdroppers around it and a UAV Jammer sends artificial noise to interfere with the eavesdroppers. We aim to maximize the physical layer secrecy rates in the system by jointly optimizing the trajectories of UAVs and their transmitting power. Considering the time-varying characteristics of channels, this problem is modeled as a Markov decision process (MDP). An improved algorithm based on double-DQN is proposed in the paper to solve this MDP problem. Simulation results show that the proposed algorithm can converge quickly under different environments, and the UAV transmitter and UAV jammers can find the optimal location correctly to maximize the information secrecy rate. It also shows that the double-DQN (DDQN) based algorithm works better than the Q-learning and deep Q-learning network (DQN).

Multi-UAV Collaborative Absolute Vision Positioning and Navigation: A Survey and Discussion

The employment of unmanned aerial vehicles (UAVs) has greatly facilitated the lives of humans. Due to the mass manufacturing of consumer unmanned aerial vehicles and the support of related scientific research, it can now be used in lighting shows, jungle search-and-rescues, topographical mapping, disaster monitoring, and sports event broadcasting, among many other disciplines. Some applications have stricter requirements for the autonomous positioning capability of UAV clusters, requiring its positioning precision to be within the cognitive range of a human or machine. Global Navigation Satellite System (GNSS) is currently the only method that can be applied directly and consistently to UAV positioning. Even with dependable GNSS, large-scale clustering of drones might fail, resulting in drone cluster bombardment. As a type of passive sensor, the visual sensor has a compact size, a low cost, a wealth of information, strong positional autonomy and reliability, and high positioning accuracy. This automated navigation technology is ideal for drone swarms. The application of vision sensors in the collaborative task of multiple UAVs can effectively avoid navigation interruption or precision deficiency caused by factors such as field-of-view obstruction or flight height limitation of a single UAV sensor and achieve large-area group positioning and navigation in complex environments. This paper examines collaborative visual positioning among multiple UAVs (UAV autonomous positioning and navigation, distributed collaborative measurement fusion under cluster dynamic topology, and group navigation based on active behavior control and distributed fusion of multi-source dynamic sensing information). Current research constraints are compared and appraised, and the most pressing issues to be addressed in the future are anticipated and researched. Through analysis and discussion, it has been concluded that the integrated employment of the aforementioned methodologies aids in enhancing the cooperative positioning and navigation capabilities of multiple UAVs during GNSS denial.

Research on Obstacle Avoidance Control of Multiple UAV Formation based on Genetic Algorithm

The UAV (Unmanned Aerial Vehicle) group cooperative formation flight technology has the advantages of wide coverage, large activity radius, strong overall search ability and high efficiency of the aircraft group. Therefore, it is suitable for various complex tasks in the military field such as battlefield environment reconnaissance, tactical attack and cooperative search. This paper proposes a consistency control strategy based on GA (Genetic Algorithm) and applies it to multi-UAV formation obstacle avoidance, which can effectively solve the collision between UAVs and between UAV formation and obstacles. Demodulate the received ground desired control command, and introduce an additional auxiliary traction acceleration through GA to avoid the local optimal solution. The auxiliary traction acceleration is related to the speed and relative position of UAV and obstacles. It can be used as a disturbance to solve the local optimal solution, and also as an auxiliary acceleration to improve the speed of avoiding moving obstacles. Finally, the rapid formation and obstacle avoidance of UAV fleet during flight are realized, and the survivability of the fleet in the battlefield environment is improved.

Cooperative Navigation for Heterogeneous Air-Ground Vehicles Based on Interoperation Strategy

This paper focuses on the cooperative navigation of heterogeneous air-ground vehicle formations in a Global Navigation Satellite System (GNSS) challenged environment and proposes a cooperative navigation method based on motion estimation and a regionally optimal path planning strategy. In air-ground vehicle formations, unmanned ground vehicles (UGVs) are equipped with low-precision inertial navigation measurement units and wireless range sensors, which interact with unmanned aerial vehicles (UAVs) equipped with high-precision navigation equipment for cooperative measurement information and use the UAVs as aerial benchmarks for cooperative navigation. Firstly, the Interacting Multiple Model (IMM) algorithm is used to predict the next moment’s motion position of the UGVs. Then regional real-time path optimization algorithms are introduced to design the motion position of the high-precision UAVs so as to improve the formation’s configuration and reduce the geometric dilution of precision (GDOP) of the configuration. Simulation results show that the Dynamic Optimal Configuration Cooperative Navigation (DOC-CN) algorithm can reduce the GDOP of heterogeneous air-ground vehicle formations and effectively improve the overall navigation accuracy of the whole formation. The method is suitable for the cooperative navigation environment of heterogeneous air-ground vehicle formations under GNSS-challenged conditions.

Topology construction and topology adjustment in flying Ad hoc networks for relay transmission

A flying ad hoc network (FANET) is formed by multiple unmanned aerial vehicles (UAVs) in an ad hoc manner in which UAVs can cooperate more effectively to complete complex tasks. In this paper, we mainly investigate the topology construction and topology adjustment for FANETs in which relay UAVs are used for relay transmission between mission UAVs and a ground control station (GCS), so that each mission UAV can communicate with the GCS through a multi-hop communication connection. Firstly, we propose an alternating optimization algorithm to optimize the routing paths and the positions of relay UAVs alternately, so as to construct a feasible FANET topology using the minimal number of relay UAVs. Then, we propose a greedy algorithm to deploy new relay UAVs to the FANET topology constructed by the alternating optimization algorithm, so as to construct a high-performance FANET topology using the given number of relay UAVs. Both the alternating optimization algorithm and the greedy algorithm need to be executed in a centralized manner, so these two algorithms can only be used for FANET topology construction. Furthermore, we propose a distributed algorithm to adjust the existing FANET topology when the positions of mission UAVs change, so as to improve the FANET network performance. This distributed algorithm can be executed in a distributed manner, so this algorithm can be used for FANET topology adjustment. The simulation results show that the algorithms proposed in this paper are superior to other baselines.

UAV Deployment Optimization for Secure Precise Wireless Transmission

This paper develops an unmanned aerial vehicle (UAV) deployment scheme in the context of the directional modulation-based secure precise wireless transmissions (SPWTs) to achieve more secure and more energy efficiency transmission, where the optimal UAV position for the SPWT is derived to maximize the secrecy rate (SR) without frequency diverse array (FDA) and injecting any artificial noise (AN) signaling. To be specific, the proposed scheme reveals that the optimal position of UAV for maximizing the SR performance has to be placed at the null space of Eves channel, which impels the received energy of the confidential message at the unintended receiver deteriorating to zero, whilst benefits the one at the intended receiver by achieving its maximum value. Moreover, the highly cost FDA structure is eliminated and transmit power is all allocated for transmitting a useful message which shows its energy efficiency. Finally, simulation results verify the optimality of our proposed scheme in terms of the achievable SR performance.

Adaptive fractional‐order nonsingular fast terminal sliding mode formation control of multiple quadrotor UAVs‐based distributed estimator

AbstractThis paper mainly solves two major problems that are unavoidable in leader–follower formation process of quadrotor UAV group: the existence of external uncertainty disturbance and communication limited between quadrotor unmanned aerial vehicle (UAV) group. To solve the problem that only one of the followers in the leader–follower formation can obtain the leader's information, an improved distributed estimator is proposed in this paper, which can accurately estimate the leader's information for each follower. In addition, in order to eliminate the influence of uncertain external disturbance on the performance of quadrotor UAV, an adaptive estimation law is designed based only on velocity and position variables. For the attitude and position subsystem of the quadrotor UAV, a sliding surface with fractional‐order term is designed. Which makes the quadrotor UAV tracking error system obtain good robustness at the stage of reaching the sliding surface and fast convergence and accurate tracking performance in the sliding stage. Based on Lyapunov stability theory, the convergence results are analyzed strictly. The results show that the algorithm can make the position distance between leader and followers converge to the desired offset. Simulation results verify the effectiveness and superiority of the control algorithm.

Attitude Control of the Quadrotor UAV with Mismatched Disturbances Based on the Fractional-Order Sliding Mode and Backstepping Control Subject to Actuator Faults

Considering mismatched disturbances, aerodynamic interference, chattering, and actuator failure in the attitude control of the quadrotor unmanned aerial vehicle (UAV), this paper establishes a new quadrotor UAV model with mismatched disturbances, based on quaternion, and designs a fault tolerant controller. First, in order to reduce the chattering of the traditional reaching law, a new reaching law based on the sigmoid function is introduced into the design. Second, the sliding mode control and backstepping control methods are adopted, based on the new fractional-order sliding mode surface when the faults occur in quadrotor UAV actuators, and parameters in the sliding mode control are adaptively adjusted. The simulation results show that the fault tolerant control method can control the attitude of UAV quickly and achieve good robustness.

Sensor Clustering Using a K-Means Algorithm in Combination with Optimized Unmanned Aerial Vehicle Trajectory in Wireless Sensor Networks.

We examine a general wireless sensor network (WSN) model which incorporates a large number of sensors distributed over a large and complex geographical area. The study proposes solutions for a flexible deployment, low cost and high reliability in a wireless sensor network. To achieve these aims, we propose the application of an unmanned aerial vehicle (UAV) as a flying relay to receive and forward signals that employ nonorthogonal multiple access (NOMA) for a high spectral sharing efficiency. To obtain an optimal number of subclusters and optimal UAV positioning, we apply a sensor clustering method based on K-means unsupervised machine learning in combination with the gap statistic method. The study proposes an algorithm to optimize the trajectory of the UAV, i.e., the centroid-to-next-nearest-centroid (CNNC) path. Because a subcluster containing multiple sensors produces cochannel interference which affects the signal decoding performance at the UAV, we propose a diagonal matrix as a phase-shift framework at the UAV to separate and decode the messages received from the sensors. The study examines the outage probability performance of an individual WSN and provides results based on Monte Carlo simulations and analyses. The investigated results verified the benefits of the K-means algorithm in deploying the WSN.

Equalizing service probability in UAV-assisted wireless powered mmWave networks for post-disaster rescue

Unmanned aerial vehicles (UAVs) are widely used in the areas where ground network infrastructure is inadequate (or crippled by accidents and disasters) to provide services for ground user equipments (UEs). However, the existing related works cannot serve ground UEs fairly. Due to the original intention of post-disaster emergency network construction with the highest goal of saving lives, fair access service for ground UEs is particularly important in this scenario. Therefore, we address this problem by jointly optimizing the number of service grids, the flight trajectory and the hovering position of a UAV. Firstly, an adaptive area division and consolidation (AADC) method is proposed to optimize the number of service grids of a UAV. Then, a new fair service policy is designed to plan the UAV flight trajectory. Finally, the hovering position of UAV is optimized in each service grid to improve service efficiency. The simulation results show that the proposed scheme both ensures fairness of services to ground UEs and improves system throughput.

A Novel Semidefinite Programming-based UAV 3D Localization Algorithm with Gray Wolf Optimization

The unmanned aerial vehicle (UAV) network has gained vigorous evolution in recent decades by virtue of its advanced nature, and UAV-based localization techniques have been extensively applied in a variety of fields. In most applications, the data captured by a UAV are only useful when associated with its geographic position. Efficient and low-cost positioning is of great significance for the development of UAV-aided technology. In this paper, we investigate an effective three-dimensional (3D) localization approach for multiple UAVs and propose a flipping ambiguity avoidance optimization algorithm. Specifically, beacon UAVs take charge of gaining global coordinates and collecting distance measurements from GPS-denied UAVs. We adopt a semidefinite programming (SDP)-based approach to estimate the global position of the target UAVs. Furthermore, when high noise interference causes missing distance pairs and measurement errors, an improved gray wolf optimization (I-GWO) algorithm is utilized to improve the positioning accuracy. Simulation results show that the proposed approach is superior to a number of alternative approaches.

Mission-based PTR triangle for multi-UAV systems flight planning

The Unmanned Aerial Vehicle (UAV) domain, especially the design of UAV cooperative systems, is one of the most important research and applied fields nowadays. The multi-UAV system design is a wealth of research points under the flight planning umbrella, including the design of communication protocols for the Flying Ad-hoc Network (FANET) connecting UAVs, and the inevitable flight design aspects which are the mission planning and path planning. With the fact that topology and routing are the communication design parts most affected by FANET's highly dynamic 3D movement, this paper introduces a design concept for multi-UAV system flight plan that has been named mission-based PTR triangle to indicate the importance of joint optimization of the three design pillars: Path planning, Topology control, and Routing strategy based on mission requirements. These aspects are very interconnected; they determine the UAV positions and relative placement and how the UAVs are connected; any of them can influence or be influenced by the other. They can be regarded as a single process and the trade-off between their parameters controls their influence. In addition, most of the flight planning operations are confined to a PTR triangle its edges represent cross-layer or joint optimization associations, results in different optimization cases to support adaptive optimality suitable to the dynamic nature of FANET, design constraints, and variety in mission scenarios. To this end, this paper sheds light on and reviews the work done on each process and on the mission analysis, including classifications, approaches, and examples of separate and cross-layered optimization. The paper drew a roadmap for flight planning employing the mission-based PTR triangle design approach with a proof-of-concept to the PTR joint optimization ideas. The paper serves as an entry point to the interested researchers in the field of UAV systems design.

3D Uplink Channel Modeling of UAV-Based mmWave Fronthaul Links for Future Small Cell Networks

In this study, we consider an unmanned aerial vehicle (UAV)-assisted heterogeneous network that is offered as a cost effective and easy to deploy solution to solve the problem related to transferring traffic of distributed small cells to the core network. For any given distribution of small cell base stations (SBSs), we first characterize an accurate millimeter wave (mmWave) channel model for SBS to networked flying platform (NFP) by taking into consideration real parameters such as UAV's vibrations, distribution of SBSs, position of UAVs in the sky, real three-dimensional (3D) antenna pattern model provided by 3GPP along with interference caused by antenna side lobes and frequency reuse. Then, for the characterized channel, we derive an analytical closed-form expression for the end-to-end signal-to-noise plus interference ratio (SINR). Based on that, we derive the closed-form expressions for the outage probability and channel capacity of the considered UAV-based mmWave uplinks. The accuracy of the derived analytical expressions is verified by Monte Carlo simulations. Finally, we investigate the effects of different channel parameters such as antenna pattern gain, strength of UAV's vibrations, UAVs' positions in the sky, distribution of SBSs, and frequency reuse on the performance of the considered UAV-based uplink channel in terms of average capacity and outage probability.

Adaptive Extended Kalman Filter-Based Fusion Approach for High-Precision UAV Positioning in Extremely Confined Environments

For unmanned aerial vehicle (UAV)-based smart inspection in extremely confined environments, it is impossible for precise UAV positioning with global positioning system, owing to the satellite signal block. Therefore, the ultrawideband (UWB)-based technology has attracted extensive attention under such circumstances. However, due to the unpredictable propagation condition and the time-varying operational environment, the localization performance oscillation caused by the changing measurement noise may lead to the instability of UAV. To mitigate the effects, in this article, a high-precision UAV positioning system which integrates the inertial measurement unit and UWB with the adaptive extended Kalman filter (EKF) is proposed. Compared with the traditional EKF-based approach, the estimated and recorded information from previous processes is exploited to adaptively estimate and further control the estimation of the noise covariance matrices for the performance improvement. Finally, simulations and experiments have been conducted in extremely confined environments. According to the results, the proposed algorithm can significantly improve the position update rate, the median positioning error, the 95 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\text{th}}$</tex-math></inline-formula> percentile positioning error, and the average standard deviation into 88 Hz, 0.102 m, 0.192 m, and 0.052 m, which is applicable for applications in focused environments.

Analysis of Maintenance Techniques for a Three-Dimensional Digital Twin-Based Railway Facility with Tunnels

In accordance with the paradigm of the 4th and Industrial Revolution, the introduction of building information modeling is expected in all areas related to railroad construction, operation and management, along with the establishment of a metaverse platform that combines big data, the Internet of Things, and artificial intelligence. The performance of tasks related to the safety and maintenance of railway facilities is aided by the use of digital systems free from physical and temporal constraints. Three-dimensional (3D) modeling and other 4th industrial technologies, such as unmanned aerial vehicles (UAVs) and light detection and ranging (LiDAR), are increasingly implemented in many types of infrastructure. With respect to railroads, the use of these methods to monitor tunnel spaces has been hindered by the limitations of modeling with UAV and inadequate Global Positioning System reception. Here, we conducted the domestic application of 4th industrial technologies to a railway tunnel; we addressed these problems using a BLK360, a fixed LiDAR device that combines two-dimensional panoramic images and a 3D point cloud method. The outcomes of this research will benefit railway operation managers by providing a platform combining a two-dimensional panoramic virtual reality (VR) image and a 3D model developed from a 3D scan framework for the maintenance of existing railway facilities (tunnels). Our approach was optimized for the maintenance and operational management of railroad facilities, as demonstrated for tunnels, because it continuously acquires time-series data that is appropriate for the maintenance of the corresponding space. In the future, this approach can be used for test tracks and operational lines.

Improvement of Hexacopter UAVs Attitude Parameters Employing Control and Decision Support Systems.

Today, there is a conspicuous upward trend for the development of unmanned aerial vehicles (UAVs), especially in the field of multirotor drones. Their advantages over fixed-wing aircrafts are that they can hover, which allows their usage in a wide range of remote surveillance applications: industrial, strategic, governmental, public and homeland security. Moreover, because the component market for this type of vehicles is in continuous growth, new concepts have emerged to improve the stability and reliability of the multicopters, but efficient solutions with reduced costs are still expected. This work is focused on hexacopter UAV tests carried out on an original platform both within laboratory and on unrestricted open areas during the start-stop manoeuvres of the motors to verify the operational parameters, hover flight, the drone stability and reliability, as well as the aerodynamics and robustness at different wind speeds. The flight parameters extracted from the sensor systems' comprising accelerometers, gyroscopes, magnetometers, barometers, GPS antenna and EO/IR cameras were analysed, and adjustments were performed accordingly, when needed. An FEM simulation approach allowed an additional decision support platform that expanded the experiments in the virtual environment. Finally, practical conclusions were drawn to enhance the hexacopter UAV stability, reliability and manoeuvrability.

Efficient 3D Positioning of UAVs and User Association Based on Hybrid PSO-K-Means Clustering Algorithm in Future Wireless Networks

Unmanned aerial vehicles (UAVs) play an important role in the future of 5G and 6G communication networks. UAV-assisted communication offers the benefits of improved network capacity and coverage. A typical communication setup is for UAVs to connect users to the core network via a backhaul channel. Some of the challenges in such a setup include user-UAV association and management of the backhaul channel. These two challenges are greatly impacted by the positioning of the UAVs in the network. In this article, we address these challenges by considering a joint UAV placement and user association problem under data rate, signal to interference and noise ratio, and bandwidth constraints. To overcome this problem, a hybrid PSO-K-means clustering algorithm is used in two stages. In the first stage, we use a K-means algorithm to cluster users and determine their horizontal locations. In the second stage, we use particle swarm optimization (PSO) to find the efficient 3D position of UAVs to maximize various network designs, namely, the network-centric approach and the user-centric approach. The performance of the proposed solution is verified using simulation results.

3D Hybrid Localization Algorithm for Mitigating NLOS Effects in Flying Ad Hoc Networks

Positions of unmanned aerial vehicles (UAVs) are typically obtained using the global positioning system (GPS). However, in GPS-denied or GPS-degraded environments, ad hoc networks with flying sensor nodes are used for UAV localization. In this study, we propose a novel three-dimensional (3D) localization algorithm for UAVs in flying ad hoc sensor networks. Interacting multiple model probability data association and finite impulse response filters are integrated in our hybrid localization algorithm. The non-line-of-sight condition can be overcome using the proposed algorithm, which is demonstrated through 3D localization simulations based on flying ad hoc networks.