Position Of UAV Research Articles

Optimization algorithm of UAV‐assisted federated learning resources based on wireless energy harvesting

AbstractFederated learning (FL) enhances data privacy and security by enabling multiple terminals to collaboratively train a global model without transferring data to a central location. To tackle the challenges of limited terrestrial communication resources and device energy in FL, a UAV‐assisted federated learning and energy collection resource optimization algorithm is proposed. This algorithm harvests the working energy of FL terminals from radio frequency signals transmitted by the UAV via wireless energy collection. Considering energy causality and limited resources, we formulate a problem aimed at minimizing the completion time of FL training tasks. As this problem is NP‐hard, we initially employ a greedy algorithm to optimize the UAV's position, then decompose it into three sub‐problems: computing resources, power control, and bandwidth allocation. We derive and establish the optimization objective function for computing resources as convex, obtaining the expression for optimal computing resources. The Brent algorithm, based on golden section interpolation, iteratively solves the power distribution sub‐problem, while the Lagrange‐quasi‐Newton algorithm optimizes bandwidth allocation for each user. Simulation results demonstrate that the proposed algorithm effectively reduces training completion time while maintaining training quality.

UAV Position Optimization for Servicing Ground Users Based on Deep Reinforcement Learning

UAV Position Optimization for Servicing Ground Users Based on Deep Reinforcement Learning

UAV Jamming Localization Against Random Eavesdropper Positions Using Cuckoo Search Algorithm

ABSTRACTUnmanned Aerial Vehicle (UAV) jammer localization refers to the process of determining the optimal location for a UAV jammer to be placed to interfere with an eavesdropper's communication with a target. In fact, the main goal is to maximize the jamming signal power at the eavesdropper's position while minimizing the jamming signal power at the target's position. This paper leverages Cuckoo Search algorithm for the secrecy rate optimization which refers to the amount of secure information transmitted from the target to the intended receiver. The model is composed with an UAV jammer, two ground nodes and an illegitimate user with unknown position. True uniform random deployment processes is used to model the randomness of the eavesdropper location. Further, a novel summed secrecy approach is proposed to determine the overall optimal UAV position. Simulations demonstrate the superiority of the novel approach versus other benchmark algorithms across various numbers of eavesdroppers.

Large-Scale Solar-Powered UAV Attitude Control Using Deep Reinforcement Learning in Hardware-in-Loop Verification

Large-scale solar-powered unmanned aerial vehicles possess the capacity to perform long-term missions at different altitudes from near-ground to near-space, and the huge spatial span brings strict disciplines for its attitude control such as aerodynamic nonlinearity and environmental disturbances. The design efficiency and control performance are limited by the gain scheduling of linear methods in a way, which are widely used on such aircraft at present. So far, deep reinforcement learning has been demonstrated to be a promising approach for training attitude controllers for small unmanned aircraft. In this work, a low-level attitude control method based on deep reinforcement learning is proposed for solar-powered unmanned aerial vehicles, which is able to interact with high-fidelity nonlinear systems to discover optimal control laws and can receive and track the target attitude input with an arbitrary high-level control module. Considering the risks of field flight experiments, a hardware-in-loop simulation platform is established that connects the on-board avionics stack with the neural network controller trained in a digital environment. Through flight missions under different altitudes and parameter perturbation, the results show that the controller without re-training has comparable performance with the traditional PID controller, even despite physical delays and mechanical backlash.

Multi-Unmanned Aerial Vehicles Cooperative Trajectory Optimization in the Approaching Stage Based on the Attitude Correction Algorithm

This study investigated the problem of multi-UAVs cooperative trajectory optimization for remote maritime targets in the approach phase. First, based on the precise location information of the cooperative target, a real-time algorithm for correcting UAV attitude angles is proposed to reduce the impact of UAV attitude angle errors and observation system errors on target positioning accuracy. Then, the attitude correction algorithm is integrated into the interacting multiple model-cubature information filter (IMM-CIF) algorithm to achieve the fusion of multi-UAVs observation information. Furthermore, an improved receding horizon optimization (RHO) method is employed to plan the cooperative observation trajectories for UAVs in real time at the target approaching stage. Finally, numerical simulations are conducted to examine the proposed attitude correction and trajectory optimization algorithm, verifying the effectiveness of the proposed method and enhancing the tracking accuracy of the remote target.

Application of fuzzy logic control theory combined with target tracking algorithm in unmanned aerial vehicle target tracking

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance.

Secure transmission of IRS-UAV buffer-aided relaying system with delay constraint

This paper investigates the secure communication between legitimate users in the presence of eavesdroppers, where the Intelligent Reflective Surface-Unmanned Aerial Vehicle (IRS-UAV) and Buffer-Aided (BA) relaying techniques are utilized to enhance secrecy performance. By jointly optimizing the link selection strategy, the UAV position, and the reflection coefficient of the IRS, we aim to maximize the long-term average secrecy rate. Specifically, we propose a novel buffer in/out stabilization scheme based on the Lyapunov framework, which transforms the long-term average secrecy rate maximization problem into two per-slot drift-plus-penalty minimization problems with different link selection factors. The hybrid Particle Swarm Optimization-Artificial Fish Swarm Algorithm (PSO-AFSA) is adopted to optimize the UAV position, and the IRS reflection coefficient optimization problem is solved by iterative optimization in which auxiliary variables and standard convex optimization algorithms are introduced. Finally, the delay constraint is set to ensure the timeliness of information packets. Simulation results demonstrate that our proposed scheme outperforms the comparison schemes in terms of average secrecy rate. Specifically, the addition of BA improves the average secrecy rate by 1.37 bps/Hz, and the continued optimizations of IRS reflection coefficients and UAV positions improve the average secrecy rate by 2.46 bps/Hz and 3.75 bps/Hz, respectively.

Resource Allocation for UAV-RIS-Assisted NOMA-Based URLLC Systems

This work focuses on maximizing the sum rate of ultra-reliable low-latency communication (URLLC) systems by leveraging unmanned aerial vehicle-mounted reconfigurable intelligent surface (UAV-RIS) to provide short packet services for users based on the non-orthogonal multiple access (NOMA) protocol. To optimize the sum rate of system, a joint optimization is performed with respect to the power allocation, UAV position, decoding order, and RIS phase shifts. As the original problem is a non-convex integer optimization problem, it is challenging to obtain the optimal solution. Therefore, approximate solutions are derived using successive convex approximation (SCA), slack variables, and penalty-based methods. The simulation results demonstrate the superiority of the proposed resource allocation algorithm compared with the benchmark algorithm with orthogonal multiple access (OMA) scheme. In addition, this work emphasizes the performance gap between the proposed communication system and the traditional Shannon communication system in terms of throughput and the performance capacity sacrificed to achieve lower latency.

ACCURACY ANALYSIS OF UAV COORDINATES USING EGNOS AND SDCM AUGMENTATION SYSTEMS

SBAS systems are applied in precise positioning of UAV. The paper presents the results of studies on the improvement of UAV positioning with the use of the EGNOS+SDCM solutions. In particular, the article focuses on the application of the model of totaling the SBAS positioning accuracy to improve the accuracy of determining the coordinates of UAVs during the realisation of a test flight. The developed algorithm takes into account the position errors determined from the EGNOS and SDCM solutions. as well as the linear coefficients that are used in the linear combination model. The research was based on data from GPS observations and SBAS corrections from the AsteRx-m2 UAS receiver installed on a Tailsitter platform. The tests were conducted in September 2020 in northern Poland. The application of the proposed algorithm that sums up the positioning accuracy of EGNOS and SDCM allowed for the improvement of the accuracy of determining the position of the UAV by 82-87% in comparison to the application of either only EGNOS or SDCM. Apart from that, another important result of the application of the proposed algorithm was the reduction of outlier positioning errors that reduced the accuracy of the positioning of UAV when a single SBAS solution (EGNOS or SDCM) was used. The study also presents the effectiveness of the proposed algorithm in terms of calculating the accuracy of EGNOS+SDCM positioning for the weighted average model. The developed algorithm may be used in research conducted on other SBAS supporting systems.

Enhancing Data Processing Methods to Improve UAV Positioning Accuracy

Enhancing Data Processing Methods to Improve UAV Positioning Accuracy

Evaluation of control techniques for quadcopter UAV attitude tracking

ABSTRACT This paper evaluates the performance of six controllers used for the attitude tracking of the quadcopter. The evaluation is done by testing the tracking performance and robustness of each controller with respect to unknown dynamics, disturbances, gain variations, and noise. These controllers include the well-known Proportional-Integral-Derivative (PID) controller to establish a baseline, the Linear Active Disturbance Rejection Controller (LADRC), the first-order Sliding Mode Controller (SMC), the second-order Super-Twisting SMC (STSMC), the Backstepping Controller (BSC), and synergetic controller. To ensure a fair and systematic evaluation, the parameters of each control method were optimised using a Particle Swarm Optimizer (PSO), incorporating a penalty term to maintain realistic control signals while minimising error. The paper details the control techniques used and describes the optimisation process. The results suggest the superiority of LADRC over the other controllers. In the conclusion section, the paper presents several prospective strategies aimed at enhancing the discussed control techniques.

Research on purely azimuthal passive positioning of unmanned aerial vehicles based on geometric analysis and three-point intersection circle positioning

With the development of UAV technology, this paper discusses the problem of the positioning model of UAVs passively receiving radio signals in UAV clusters. For the case of a circular formation, two possible point distributions are analysed, and the positioning model of UAVs passively receiving signals is established by geometric analysis. In the case where the positions of UAVs numbered FY00 and FY01 are known, the problem of determining how many UAVs need to transmit signals in order to effectively locate a certain UAV with a slightly deviated position is addressed. The paper goes on to prove that only one UAV of unknown number is required to transmit signals for effective localization by analyzing the structure of a positive nine-sided shape on a circle. The article establishes a model of the problem through geometric analysis and proposes a corresponding solution, which provides some theoretical support for the formation flight of UAV clusters.

Head and neck injury risk resulting from impact with small unmanned aerial vehicles: effect of impact speed, angle, and location

It is necessary to analyse the injury risk caused by UAV to establish management standards. A lightweight quadrotor UAV was used to assess the risk of human head and neck injury, taking the impact speed, UAV position, and impact angle into account. First, the MAVIC2 UAV- Hybrid III dummy impacting FE model was developed and verified against the UAV impact tests. Second, lots of simulations were conducted under different impact scenarios to analyse the head and neck injury. Under vertical impact condition, HIC and Nij grew exponentially as the impact speed increased. While under the horizontal impact condition, HIC increased in the form of the power function and Nij increased exponentially. Under vertical impact condition at 20 m/s (maximum flight speed, corresponding to the impact energy of 181.4 J), the HIC was 54% of the threshold, while the Nij was 88% of the threshold, which illustrated that the injury risk of the neck was high, while that of the head was low. Under horizontal impact condition at 20 m/s (181.4 J), the HIC was 51% of the threshold, and the Nij was 24% of the threshold, which illustrated that the injury risk of both the head and neck was low. HIC declined exponentially as the impact angle increased, whereas Nij increased in the form of the power function. Among all the impact positions, the UAV’s arm caused the least injury risk, while the transverse falling attitude between the two arms and the reverse vertical falling attitude generated higher injury risks.

Relative positioning method for unmanned aerial vehicles in complex electromagnetic environment

In view of the problem that satellite navigation and positioning systems may fail or be unreliable in complex electromagnetic environments, which may lead to divergence of UAV positioning, the inertial navigation systems working for a long time may lead to increasing errors. This paper adopts a relative navigation algorithm that combines airborne inertial navigation systems and inter-aircraft relative information measurement. The algorithm requires each UAV to load relative navigation equipment and inertial navigation systems and acquire and fuse relative navigation information through inter-aircraft communication. In this relative navigation algorithm, the host and wing plane can transmit their respective navigation information and relative navigation information to each other, and fuse information from different sources through a Kalman filtering algorithm to enhance the accuracy of relative positioning data. With the help of corrected and feedback relative positioning data, the relative location of the host and wingman is achieved. The actual simulation results show that the relative position RMSE of the host, Wing Plane 1, and Wing Plane 2 in the X, Y, and Z dimensions are 1.345 m, 0.7388 m, 1.645 m, 2.167 m, 1.201 m, and 2.069 m, respectively. It is apparent that this scheme improves the relative navigation performance and robustness of the UAV group.

Adaptive UAV Navigation Method Based on AHRS.

To address the inaccuracy of the Constant Acceleration/Constant Velocity (CA/CV) model as the state equation in describing the relative motion state in UAV relative navigation, an adaptive UAV relative navigation method is proposed, which is based on the UAV attitude information provided by Attitude and Heading Reference System (AHRS). The proposed method utilizes the AHRS output attitude parameters as the benchmark for dead reckoning and derives a relative navigation state equation with attitude error as process noise. By integrating the extended Kalman filter output for relative state estimation and employing an adaptive decision rule designed using the innovation of the filter update phase, the proposed method recalculates motion states deviating from the actual motion using the Tasmanian Devil Optimization (TDO) algorithm. The simulation results show that, compared with the CA/CV model, the proposed method reduces the relative position errors by 12%, 23%, and 32% in the X, Y, and Z directions, respectively, and that it reduces the relative velocity errors by 350%, 330%, and 300%, respectively. There is a significant improvement in the relative navigation accuracy.

Hardware-in-the-loop simulation test platform for UAV flight control system

The flight control system is the integrated command center and core component of the UAV system. Aiming at the problems of poor accuracy and low efficiency in the field measurement of flight control system parameters, combined with the configuration of the flight attitude sensor-vertical gyroscope, hardware-in-the-loop simulation technology, and integrated automatic test methods are adopted. The hardware-in-the-loop simulation test platform for the UAV flight control system composed of a low-cost, high-precision miniaturized UAV attitude calibration platform and a control piston deflection angle test device is designed. The main controller replaces the ground master control station of the UAV and directly sends remote control commands to the aircraft. By changing the attitude of the aircraft, and measuring the voltage of the control signal, the feedback signal, and the rudder angle, the comprehensive performance test of the parameters of the flight control system is realized. The performance test of the platform shows that the platform can meet the test requirements of angular position, system voltage, and control piston deflection angle.

Onboard sound emission measurements of unmanned aerial vehicles for psychoacoustic experiments

With the prospect of small UAVs being more commonly used for tasks like surveillance or parcel transport within inhabited areas, demand for noise regulations arises. The noise assessment of UAV operations can be investigated through laboratory listening experiments using auralized sound stimuli. Sound recordings of source signals for dynamic flight operations have to be performed outdoors where measurements with stationary microphones impose uncertainties in backpropagation and suffer from bad signal-to-noise ratio due to the weak source and the presence of ambient sounds. To overcome these difficulties, this contribution presents an approach for acquiring UAV source signals using onboard microphones. A lightweight setup for time synchronous recordings of sound using MEMS microphones, the UAV position and the rotational speed of each rotor was developed and applied to two quadcopters of 0.9 and 6.3 kg mass. Source directivity and propagation effects were added to the source signals to obtain clean sound pressure signals at virtual listener positions. The resulting stimuli were successfully used in a listening experiment on short-term noise annoyance.

An Integration visual navigation algorithm for urban air mobility

This paper presents an integration visual navigation algorithm called PnP-ORBSLAM for UAV position estimation in Urban Air Mobility (UAM). ORBSLAM is a popular and benchmark algorithm for vision based navigation applications. The proposed method improve the performance of ORBSLAM by adding a post-processing marker recognition phase to the model. Based on the features extracted from the markers, PnP algorithm is introduced to estimate the position of the monocular camera. The position estimation accuracy of the UAV is supposed to be improved by adding the position information of the camera to the model. Experiment is carried out based on Airsim simulation platform. Results show that the PnP-ORBSLAM algorithm can improve the three-dimensional accuracy by a margin of 5.38 % compared with ORBSLAM. In addition, the process speed of the proposed method can reach about 28 frames per second. It means that the PnP-ORBSLAM algorithm can work in real-time.

A Novel Sea Target Tracking Algorithm for Multiple Unmanned Aerial Vehicles Considering Attitude Error in Low-Precision Geodetic Coordinate Environments

Geodetic coordinate information and attitude information of the observation platform are necessary for multi-UAV position alignment and target tracking. In a complex sea environment, the navigation equipment of a UAV is susceptible to interference. High-precision geodetic coordinate information and attitude information are difficult to obtain. Aiming to solve the above problems, a low-precision geodetic coordinate real-time systematic spatial registration algorithm based on multi-UAV observation and an improved robust fusion tracking algorithm of multi-UAV to sea targets considering attitude error are proposed. The spatial registration algorithm obtains the observation information of the same target based on the mutual observation information. Then, geodetic coordinate systematic error is accurately estimated by establishing the systematic error estimation measurement equation. The improved robust fusion tracking algorithm considers the influence of UAV attitude error in the observation. The simulation experiment and practical experiment show that the algorithm can not only estimate systematic error accurately but also improve tracking accuracy.

Stable flight control of UAV under atmospheric turbulence

Unmanned aerial vehicles will be subjected to various environmental disturbances such as atmospheric turbulence during the execution of tasks, which will seriously affect the flight safety of UAV. By establishing UAV flight dynamics model and atmospheric turbulence model, and using PID algorithm to design controller, this paper studies the attitude and altitude control effect of UAV under atmospheric turbulence disturbance. The simulation results show that PID algorithm can effectively control the UAV's stable flight under atmospheric turbulence and ensure the safety of UAV.