Position Of Unmanned Aerial Vehicle Research Articles

UAV Localization in Low-Altitude GNSS-Denied Environments Based on POI and Store Signage Text Matching in UAV Images

Localization is the most important basic information for unmanned aerial vehicles (UAV) during their missions. Currently, most UAVs use GNSS to calculate their own position. However, when faced with complex electromagnetic interference situations or multipath effects within cities, GNSS signals can be interfered with, resulting in reduced positioning accuracy or even complete unavailability. To avoid this situation, this paper proposes an autonomous UAV localization method for low-altitude urban scenarios based on POI and store signage text matching (LPS) in UAV images. The text information of the store signage is first extracted from the UAV images and then matched with the name of the POI data. Finally, the scene location of the UAV images is determined using multiple POIs jointly. Multiple corner points of the store signage in a single image are used as control points to the UAV position. As verified by real flight data, our method can achieve stable UAV autonomous localization with a positioning error of around 13 m without knowing the exact initial position of the UAV at take-off. The positioning effect is better than that of ORB-SLAM2 in long-distance flight, and the positioning error is not affected by text recognition accuracy and does not accumulate with flight time and distance. Combined with an inertial navigation system, it may be able to maintain high-accuracy positioning for UAVs for a long time and can be used as an alternative to GNSS in ultra-low-altitude urban environments.

Joint Trajectory Planning, Service Function Deploying, and DAG Task Scheduling in UAV-Empowered Edge Computing

Efficient task scheduling plays a key role in unmanned aerial vehicle (UAV)-empowered edge computing due to the limitation in energy supply and computation resource on the UAV platforms. This problem becomes much more complicated when the processing-dependent tasks that can be described as directed acyclic graphs (DAGs) and each of their components can only be processed on a virtual machine or container that deploys the desired service function (SF). In this paper, we first build an optimization problem that aims to minimize the completion time of all DAG tasks subject to constraints including task dependency, computation resource occupied by the UAVs, etc. To tackle this problem, a genetic algorithm-based joint deployment and scheduling algorithm, named GA-JoDeS, is put forward, since solving the established 0–1 integer programming problem in polynomial time is infeasible. Subtask offloading decision and UAV position are encoded into the chromosome in the GA-JoDeS algorithm, and the fitness value of an individual is decided by the maximum completion time of all DAG tasks. Through selection, crossover, and mutation, the GA-JoDeS algorithm evolves until it determines the individual with the optimal fitness value as the suboptimal solution to the problem. To evaluate the performance of the proposal, a series of simulations is conducted, and three traditional methods are chosen as comparison benchmarks. The results show that the GA-JoDeS algorithm can convergence quickly, and it can effectively reduce the completion time of DAG tasks with different parameter settings.

Track Planning for Damage Detection on the Building Enclosure by UAV Considering Image Detection

Considering the rapid acquisition and detection task of building enclosure damage information and the natural environment characteristics of building enclosure, a new unmanned aerial vehicle (UAV) track planning framework considering image detection and UAV dynamic characteristics is proposed. Firstly, considering the position of UAV in the task space for track planning, combined with the characteristics of the sensor detection equipment carried by UAV, and the task requirements of extracting and identifying the damage edge features and texture features on the shell of the building; the detection efficiency and detection safety of UAV are improved to the maximum extent. Secondly, the Improved Circle Chaos Initialization Strategy, Adaptive Weight Strategy and Elite Perturbation Mechanism are introduced to solve the problem that the traditional honey badger algorithm (HBA) is easy to fall into local optimal solution and to ensure that the designed algorithm is more in line with the inspection planning requirements of the outer surface of the building. Furthermore, improved honey badger algorithm (IHBA) is compared with particle swarm optimization (PSO), whale optimization algorithm (WOA) and traditional badger algorithm in multi-dimensional horizontal through design simulation experiments, which shows that the design algorithm in this paper is efficient in solving the damage information collection of building enclosure.

Distributed and Collective Intelligence for Computation Offloading in Aerial Edge Networks

Unmanned aerial vehicles (UAVs) with integrated computing platforms can be used to provide computing offloading services for ground user equipments (UEs) with limited local computing capabilities, especially in remote areas. In this paper, we focus on the task offloading in an aerial edge network (AEN) assisted by a UAV. We aim at minimizing the sum energy consumption of all UEs by the joint optimization of the task offloading decisions and the UAV position under the constraints of the latency and the total energy of UAV. The formulated optimization problem is a mixed-integer nonconvex problem and involves coupling of many optimization variables. To address this challenge, we first transform the original optimization problem into a linear convex optimization problem via reformulation linearization technology, and then the alternating direction method of multipliers (ADMM) algorithm is proposed to achieve the approximate optimal solution. Numerical results confirm that the proposed ADMM algorithm can effectively reduce the total of energy consumption of UEs and ensure the continuous operation of the UEs.

Unmanned Aerial Vehicle Position Estimation Augmentation Using Optical Flow Sensor

Flight control of unmanned aerial vehicle (UAV) requires reliable measurements of UAV’s position and attitude. Optical flow sensor is able to detect UAV’s motion with respect to the ground, and thus it can be incorporated in the integrated navigation system to enhance the positioning accuracy. However, the commonly used measurement model of optical flow sensor is actually fit for continuous-time condition only, since it defines the optical flow as the instantaneous velocity of a pixel on the image plane. For commercial optical flow sensors that work under discrete-time condition, a novel measurement model is proposed in this paper, which gives a vectorized symmetrical description of the optical flow measurement between every two successive frames in the image sequence. Moreover, a cubature transform-based data fusion scheme is also presented in this paper, which can directly augment the UAV’s position estimation with optical flow data, rather than extracting UAV’s velocity information from optical flow for dead-reckoning, and hence it can be easily added to the UAV’s navigation system without changing the existing algorithm flow. Flight tests are conducted using a quadcopter UAV that equipped with PIXHAWK autopilot and PX4Flow optical flow sensor, and the test results prove that the proposed optical flow model and data fusion scheme can effectively improve the accuracy of UAV position estimation in various outdoor environments.

Multi-Objective Optimization for EE-SE Tradeoff in Space-Air-Ground Internet of Things Networks

The Internet of Things (IoT) has become increasingly popular, and its communication requirements have grown beyond what traditional ground networks can handle. The space–air–ground (SAG) integrated network has been proposed as a potential solution, where unmanned aerial vehicles (UAVs) collect data from IoT devices and transmit them to satellites. However, the limited energy of UAVs is one of the key factors restricting communication performance, so it is necessary to consider communication energy efficiency (EE). In addition, the improvement of EE will bring about the decline of spectral efficiency (SE). Therefore we consider the tradeoff between EE and SE. Considering a SAG-IoT network, the focus of the paper is to optimize sub-channel selection, power control, and UAV position deployment to maximize EE and SE of the network, which is a multi-objective optimization (MOO) problem. On this premise, we try to improve the data throughput between IoT devices and UAVs. To solve this MOO problem, we use the ϵ-constraint to convert it into a single-objective optimization problem. We then employ various optimization algorithms such as the Dinkelbach algorithm, successive convex approximation algorithm, Lagrange dual algorithm, and block coordinate descent algorithm to solve the mixed integer non-convex problem. Simulation results show that the proposed algorithm converges to at least one sub-optimal solution.

Generic Edge Computing System for Optimization and Computation Offloading of Unmanned Aerial Vehicle

Several Unmanned Aerial Vehicles (UAVs) offer ground equipment low-latency edge computing services. On the other hand, low-Earth orbit satellites offer all-encompassing cloud computing services. The officially stated joint optimization problem is a mixed nonlinear programming problem, leading to the development of a two-layer optimization technique. It is suggested that a particle swarm optimization (PSO) algorithm can be used in the upper layer of the algorithm along with genetic algorithm (GA) operators to optimize the UAV's deployment position. The greedy algorithm is applied to optimize the offloading of processing jobs in the lowest layer of the algorithm. The feasibility and effectiveness of the proposed strategy have been validated by numerous numerical simulation tests. The findings show that it is similar to other benchmark algorithms. The proposed approach can also reduce the usual job response time of the system.

Secure Transmission Design for Aerial IRS Assisted Wireless Networks

Combining intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) offers a new degree of freedom to improve the coverage performance. However, it is more challenging to secure the air-ground transmission, due to the line-of-sight (LoS) links established by UAV. Since IRS is a promising solution for wireless environment reconfiguration, in this paper, we propose an aerial IRS-assisted secure transmission design in wireless networks. In particular, an access point (AP) equipped with a uniform planar array serves several single-antenna legitimate users in the presence of multiple single-antenna eavesdroppers, whose precise positions are unknown. An IRS is mounted on the UAV to help establish desired virtual LoS links between the AP and legitimate users, while ensuring their security. We aim to maximize the worst-case sum secrecy rate by jointly optimizing the hovering position of UAV, the transmit beamforming of AP and the phase shifts of IRS, subject to the requirement of minimum rate for legitimate users. To tackle this non-convex problem, we first decompose it into three subproblems, which are transformed into convex ones via successive convex approximation. An alternating algorithm is then proposed to solve them iteratively. Simulation results show the effectiveness of the proposed scheme and the security improvement by the joint optimization.

METHODOLOGY TO IMPROVE THE ACCURACY OF DETERMINING THE POSITION OF UAVS EQUIPPED WITH SINGLE-FREQUENCY RECEIVERS FOR THE PURPOSES OF GATHERING DATA ON AVIATION OBSTACLES

Low-altitude photogrammetric studies are often applied in detection of aviation obstacles. The low altitude of the Unmanned Aerial Vehicle (UAV) flight guarantees high spatial resolution (X, Y) of the obtained data. At the same time, due to high temporal resolution, UAVs have become an appropriate tool for gathering data about such obstacles. In order to ensure the required accuracy of orientation of the photogrammetric block, Ground Control Points (GCPs) are measured. The recently introduced UAV positioning solutions that are based on Post-Processing Kinematic (PPK) and Real Time Kinematic (RTK) are known to effectively reduce, or, according to other sources, even completely eliminate the necessity to conduct GCP measurements. However, the RTK method involves multiple limitations that result from the need to ensure continuous communication between the reference station and the rover receiver. The main challenge lies in achieving accurate orientation of the block without the need to conduct time-consuming ground measurements that are connected to signalling and measuring the GCPs. Such solution is required if the SPP code method is applied to designation the position of the UAV. The paper presents a research experiment aimed at improving the accuracy of the determination of the coordinates of UAV for the SPP method, in real time. The algorithm of the SPP method was improved with the use of IGS products.

Unmanned aerial vehicle localization for device-to-device communication in fifth generation networks using modified penguin search optimization

The localization of multiple Unmanned Aerial Vehicle (UAV) in Device to Device (D2D) communication has been investigated for Internet of Things (IoT) based Fifth Generation (5 G) network. The localization in heterogeneous UAV-D2D network is formulated using bioinspired Modified Penguin Search Optimization (MPGSO) algorithm. In MPGSO, the reduction in localization error is carried by tracking the position of UAV. The intra-UAV and inter-UAV communication search strategy is realized in accordance to inter-group and intra-group communication. The proposed localization by means of UAV exploration allows the fast convergence towards the local optimum. In addition to intensification in MPGSO, the diversification of nodes leads to the exploitation of UAV nodes that have travelled from the actual position. This avoids the convergence towards the local optima. The localization with MPGSO provide an improvement of 3.7%, 5.75% and 7.8% as compared to conventional artificial bee colony, particle swarm optimization and genetic algorithm, respectively.

Federated learning via over-the-air computation in IRS-assisted UAV communications

Intelligent reflective surface (IRS) and unmanned aerial vehicle (UAV) communication are two key technologies in the sixth generation of mobile communication (6G). In this paper, IRS is equipped on UAV to form aerial IRS, which can achieve 360° panoramic full-angle reflection and flexible deployment of IRS. In order to achieve high-quality and ubiquitous network coverage under data privacy and low latency requirements, we propose an Federated learning (FL) network via Over-the-Air computation (AirComp) in IRS-assisted UAV communications. Our goal is to minimize the worst-case mean square error (MSE) by jointly optimizing the IRS phase shift, denoising factor for noise suppression, the user’s transmission power, and UAV trajectory. Optimizing and quickly adjusting the UAV position and IRS phase shift, it flexibly assists the signal transmission between users and base stations (BS). In order to solve this complex non-convex problem, we propose a low-complexity iterative algorithm, which divides the original problem into four sub-problems, respectively using the semi-definite programming (SDP) method, slack variable introduction method, successive convex approximation (SCA) method to solve each sub-problem. Through the analysis of simulation results, our proposed design scheme is obviously better than other benchmark schemes.

Robust Channel Estimation Scheme for Multi-UAV MmWave MIMO Communication with Jittering

In unmanned aerial vehicle (UAV)-assisted millimeter-wave (mmWave) communications, the communication performance is significantly degraded by UAV jitter. We formulate a UAV-assisted mmWave channel model with hybrid beamforming for the impacts of UAV jitter. Then, we derive the distribution of angle of arrivals/departures (AOAs/AODs) with random fluctuation of the UAV attitude angle. We develop an iterative reweight-based robust scheme as the super-resolution AOAs/AODs estimation method. Specifically, we introduce the partially adaptive momentum (Padam) estimation method to optimize the objective function of the jittering UAV mmWave massive multi-input multioutput (MIMO) system. Finally, compared with existing channel estimation schemes, the proposed UAV mmWave channel estimation method can achieve robust super-resolution performance in AOAs/AODs and path gains estimation with numerical results. Therefore, the proposed channel estimation scheme is very suitable for UAV mmWave massive MIMO communications with jittering.

The usefulness of sensor fusion for unmanned aerial vehicle indoor positioning

PurposeIn this study, an indoor sensor information fusion positioning system of the quadrotor unmanned aerial vehicle (UAV) was investigated to solve the problem of unstable indoor flight positioning.Design/methodology/approachThe presented system was built on Light Detection and Ranging (LiDAR), Inertial Measurement Unit (IMU) and LiDAR-Lite devices. Based on this, one can obtain the aircraft's current attitude and the position vector relative to the target and control the attitudes and positions of the UAV to reach the specified target positions. While building a UAV positioning model relative to the target for indoor positioning scenarios under limited Global Navigation Satellite Systems (GNSS), the system detects the environment through the NVIDIA Jetson TX2 (Transmit Data) peripheral sensor, obtains the current attitude and the position vector of the UAV, packs the data in the format and delivers it to the flight controller. Then the flight controller controls the UAV by calculating the posture to reach the specified target position.FindingsThe authors used two systems in the experiment. The first is the proposed UAV, and the other is the Vicon system, our reference system for comparison purposes. Vicon positioning error can be considered lower than 2 mm from low to high-speed experiments. After comparison, experimental results demonstrated that the system could fully meet the requirements (less than 50 mm) in real-time positioning of the indoor quadrotor UAV flight. It verifies the accuracy and robustness of the proposed method compared with that of Vicon and achieves the aim of a stable indoor flight preliminarily.Originality/valueVicon positioning error can be considered lower than 2 mm from low to high-speed experiments. After comparison, experimental results demonstrated that the system could fully meet the requirements (less than 50 mm) in real-time positioning of the indoor quadrotor UAV flight. It verifies the accuracy and robustness of the proposed method compared with that of Vicon and achieves the aim of a stable indoor flight preliminarily.

Downlink Interference Analysis of UAV-Based mmWave Fronthaul for Small Cell Networks

In this paper, an unmanned aerial vehicles (UAV)-based heterogeneous network is studied to solve the problem of transferring massive traffic of distributed small cells to the core network. First, a detailed three-dimensional (3D) model of the downlink channel is characterized by taking into account the real antenna pattern, UAVs' vibrations, random distribution of small cell base stations (SBSs), and the position of UAVs in 3D space. Then, a rigorous analysis of interference is performed for two types of interference: intra-cell interference and inter-cell interference. The interference analysis results are then used to derive an upper bound of outage probability on the considered system. Using numerical results show that the analytical and simulation results match one another. The results show that, in the presence of UAV's fluctuations, optimizing radiation pattern shape requires balancing an inherent tradeoff between increasing pattern gain to reduce the interference as well as to compensate large path loss at mmWave frequencies and decreasing it to alleviate the adverse effect of a UAV's vibrations.

Adaptive attitude control of UAV based on neural network compensation

Aiming at the problem of unmanned aerial vehicle (UAV) attitude control with insufficient robustness and anti-disturbance, this paper proposes a UAV attitude control method combining incremental nonlinear dynamic inverse (INDI) and neural network (NN) adaptive compensation. Firstly, the attitude model of UAV with time scale separation is proposed. On this basis, the INDI control law is derived, and the angular acceleration information required by INDI method is feedback by the filter. Then, the adaptive NN compensation structure is introduced to eliminate the uncertainty caused by model mismatch, sensor delay and external disturbance. Finally, the UAV simulation is carried out. The result shows that this method improves robustness and anti-disturbance of original INDI method without changing structure and parameters of controller, and has a certain adaptive compensation ability for system uncertainty.

UAV High-Voltage Power Transmission Line Autonomous Correction Inspection System Based on Object Detection

With the development of technology, unmanned aerial vehicles (UAVs) are playing an increasingly important role in the inspection of high-voltage power transmission line. The traditional inspection method relies on the operator to manually control the drone for inspection. Although many companies are using real-time dynamic carrier phase differencing technology to achieve high-precision positioning of UAVs, when UAVs fly autonomously at high altitudes to photograph specific objects, the objects tend to deviate from the center of the picture. To address this error, in this paper, an autonomous UAV inspection system based on object detection is designed. (1) To detect inspection objects, the corresponding dataset is established on the basis of the UAV autonomous inspection task. (2) To obtain the position information of the target object, a lightweight object detector based on the YOLOX network model is designed. First, the backbone is replaced with MobileNetv3. Next, in the neck structure, the Ghost module is introduced and depthwise convolution is applied instead of normal convolution. Then, to embed the location information into the channel attention, coordinate attention is introduced after the output feature layer of the backbone, enabling the lightweight network to operate on a larger area of focus. Finally, to improve the accuracy of the bounding box regression, the α-DIOU loss function is introduced. (3) To obtain the best image acquisition position, the results of object detection combined with the real-time status of the UAV are used. (4) To enable the UAV to complete the final corrections, position control and altitude control are used. Compared to the original YOLOX tiny, the new model improves the mAP_0.5:0.95 metric by about 2 percentage points, with a significant reduction in the number of parameters and computation, while running at 56 FPS on Nvidia NX. This system can effectively solve the problem of the target deviating from the center of the picture when the UAV takes pictures during a high-altitude autonomous inspection, verified by many actual flight experiments.

Dual-antenna GPS-based UAV Attitude Determination Method and Accuracy Analysis

Accurate attitude information of small-sized unmanned aerial vehicles (UAVs) is required to obtain absolute azimuth information of a target via an airborne electro-optical imaging system. The direct attitude solution method of dual-antenna Global Position System (GPS) is investigated in this paper to address this need. We analyzed the attitude measurement principle of the direct method, gave the error estimation model, and estimated the inner and outer precision through simulation. The simulation results demonstrated that to achieve the requirement of UAV yaw angle error within 1°, the baseline length should be no less than 1 m. The applicability of the model and algorithm was confirmed.

Multiobjective 3-D UAV Movement Planning in Wireless Sensor Networks Using Bioinspired Swarm Intelligence

The use of unmanned aerial vehicles (UAVs) is a promising solution to efficiently acquire data in large-scale wireless sensor networks (WSNs). In a wide-area WSN environment, UAV path planning is one of the challenging issues in solving the optimization problem to achieve complex and multiple objectives under various constraints related to UAV operation. In this paper, we propose a novel asynchronous UAV path planning mechanism for multi-objective UAV operation. In a three-dimensional sensor field, a grid-based sensor field and information gathering model is introduced. We define a UAV coverage area where line-of-sight communication is possible between a UAV and the sensors and propose a method to quickly find the grid-cells within the coverage area. We define a multi-purpose fitness function that maximizes the value of the acquired sensing information and at the same time minimizes the time and energy required for UAV operation. The value of the sensing information reflects the sensor density for each sensor type within UAV coverage, as well as changes in the sensing information values over time. In the proposed method, time and energy objective functions are learned by considering location-dependent communication link quality, sensor density, and the next UAV locations. The optimum UAV position and the movement schedule of each UAV are asynchronously derived using the proposed particle swarm optimization (PSO) algorithm with several UAV operational constraints. The experiment results demonstrate that the proposed method can maximize the utility of the objective function and achieve fast convergence in finding the optimal solution compared with other methods.

Deployment and Robust Hybrid Beamforming for UAV MmWave Communications

We deploy an unmanned aerial vehicle (UAV) equipped with a large-scale uniform planar array (UPA) to serve multiple ground users in millimeter-wave band. Particularly, the practical UAV jitter is carefully considered, which may affect the beam gains and impact the communication quality. To provide a stable service, we first model the attitude change of the UPA caused by UAV jitter. Then, an optimization problem is formulated to maximize the minimum achievable rate of the users by optimizing the position and robust hybrid beamforming of the UAV. To solve the non-convex problem with highly coupled variables, a two-stage optimization strategy is developed. The first stage aims to decouple the beamforming from the original problem and design the UAV deployment under the assumption of an ideal beam pattern. The second stage aims to design robust hybrid beamforming with the obtained UAV position. Specifically, we first design analog beamforming for wide beams to cover the potential jitter angle range for each user via a chirp sequence-inspired method. Then, with equivalent channel estimation, we design digital beamforming by combining zero-forcing and water-filling power allocation algorithms. Extensive simulation results show the performance superiority of the proposed solution compared to the benchmark algorithms.

Augmentation for unmanned aerial vehicle position estimation using MARG and optical flow sensors

The optical flow sensor can detect the movement of the unmanned aerial vehicle (UAV) on the ground; thus, it is widely used for UAV flight control. The commonly used pinhole model of optical flow sensor describes the optical flow measurement in linear and angular velocities. It is not suitable in the case of discrete-time processing. A novel measurement model for the optical flow sensor is proposed, which directly gives the relationship between the optical flow and the UAV’s translational/angular motions in each sampling period. A data fusion scheme based on cubature transform is also presented, which can augment UAV’s position estimation using optical flow data. The proposed method is proven to be effective through flight tests on UAVs.