Position Of UAV Research Articles

Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.

UAV attitude estimation based on MARG and optical flow sensors using gated recurrent unit

Three-dimensional attitude estimation for unmanned aerial vehicles is usually based on the combination of magnetometer, accelerometer, and gyroscope (MARG). But MARG sensor can be easily affected by various disturbances, for example, vibration, external magnetic interference, and gyro drift. Optical flow sensor has the ability to extract motion information from image sequence, and thus, it is potential to augment three-dimensional attitude estimation for unmanned aerial vehicles. But the major problem is that the optical flow can be caused by both translational and rotational movements, which are difficult to be distinguished from each other. To solve the above problems, this article uses a gated recurrent unit neural network to implement data fusion for MARG and optical flow sensors, so as to enhance the accuracy of three-dimensional attitude estimation for unmanned aerial vehicles. The proposed algorithm can effectively make use of the attitude information contained in the optical flow measurements and can also achieve multi-sensor fusion for attitude estimation without explicit mathematical model. Compared with the commonly used extended Kalman filter algorithm for attitude estimation, the proposed algorithm shows higher accuracy in the flight test of quad-rotor unmanned aerial vehicles.

GPS 비가용 환경을 고려한 점검 무인비행체의 보조 측위센서 설계 개념

This paper suggests a novel localization sensor for substation inspection UAVs carrying out their missions under a GPS-denied environment. The proposed sensor consists of a transmitter located at the known position and a cruciform receiver array which enables the UAV to measure the time difference of arrivals. For such case, the UAV localization problem can be cast into a passive nonlinear state estimation using the range difference (RD) information expressed by a nonlinear function of the UAV position of interest. To avoid the performance degradation due to the inherent nonlinearity of the problem, we reformulate the localization problem in the setting of a linear robust state estimation. Ensuring the robustness against imperfect RD measurement noise statistics which is cruicial for determining filter design parameters, a modified robust weighted least squares estimator is designed by considering the geometric constraint between the state variables as an additional information. Experimental results show that the proposed design concept of a UAV localization sensor is applicable in practice.

Pose Measurement for Unmanned Aerial Vehicle Based on Rigid Skeleton

Pose measurement is a necessary technology for UAV navigation. Accurate pose measurement is the most important guarantee for a UAV stable flight. UAV pose measurement methods mostly use image matching with aircraft models or 2D points corresponding with 3D points. These methods will lead to pose measurement errors due to inaccurate contour and key feature point extraction. In order to solve these problems, a pose measurement method based on the structural characteristics of aircraft rigid skeleton is proposed in this paper. The depth information is introduced to guide and label the 2D feature points to eliminate the feature mismatch and segment the region. The space points obtained from the marked feature points fit the space linear equation of the rigid skeleton, and the UAV attitude is calculated by combining with the geometric model. This method does not need cooperative identification of the aircraft model, and can stably measure the position and attitude of short-range UAV in various environments. The effectiveness and reliability of the proposed method are verified by experiments on a visual simulation platform. The method proposed can prevent aircraft collision and ensure the safety of UAV navigation in autonomous refueling or formation flight.

Rancang Bangun Sistem Komunikasi antar UAV untuk Formasi UAV

UAV formation is one of techology development in UAV world. The UAV formation can be utilized in various fields including agriculture, mapping and defense security (HANKAM). Communication is one of the important components in making UAV formations. To make UAV formations, position and direction data (AHRS) from each UAV will be sent to other UAVs so that when forming UAV formation, no collisions occur between UAVs. This study uses a star topology like the one on a computer network. This method involves two or more UAVs, both of which will exchange data. In this study the data sent is in the form of a dummy data package using the xbee module. The purpose of this study is the initial model of communication between UAVs which can later be developed into more complex communication models for many UAVs. The results obtained in the form of maximum communication distance between UAVs is 50m and the recommended optimal distance is 10-20m.

Mathematical framework for 5G‐UAV relay

AbstractUnmanned aerial vehicle's (UAVs) small size, long‐lasting battery, and flexible trajectory have produced widespread applications in radio communication systems, especially its usage as a cellular tower (gNB) for 5G services. At the same time, the mmWave's enormous available bandwidth makes it the primary contender for next‐generation communication networks. This article aims to synthesize and assess the usage of mmWave concurrently in backhaul and access links for amplify‐and‐forward (AF) relay installed on the UAV. A mathematical framework is developed for the received power of the user equipment (UE) obtained in relay path (gNB‐UAV‐UE) and direct path (gNB‐UE) via; (i) free space, and (ii) log‐distance path loss model. The International Telecommunication Union recommended city model is used to assess the likelihood for LoS and NLoS paths in four urban environments; (i) high‐rise urban, (ii) dense urban, (iii) urban, and (iv) suburban. Furthermore, various parameters, that is, UAV's position and amplification factor are stipulated to optimize the efficiency of a UAV‐based AF relay for enhanced users coverage.

Improving Energy Efficiency in UAV Attitude Control using Deep Reinforcement Learning

Improving Energy Efficiency in UAV Attitude Control using Deep Reinforcement Learning

A Unified SO(3) Approach to the Attitude Control Design for Quadrotors

This paper proposes a two-phased problem reformulation approach to solve the quadrotor UAV attitude control problem. Given an initial attitude, the objective is to design a control to drive it toward a desired value. The first phase begins with the definition of three types of errors pertaining to attitude discrepancy, followed by the utilization of virtual control with which the dynamics of quadrotor’s attitude and angular velocity are then unified under the framework of SO(3), rendering the original control problem much more transparent to tackle. Through cancellation of certain unwanted terms emerging from the unification process, entry-wise treatment of the remaining matrix dynamics is then employed in the second phase to transform it further into the stabilization problem of a 3-dimensional linear time-invariant system that is fairly easy to solve since to which standard feedback designs such as linear quadratic regulator and pole placement are readily applicable. In addition to the classical designs, a specialized and structurally simple controller is provided to reduce the number of gains. To account for system parametric uncertainties, external disturbances, and sensor measurement errors and noises, a second controller based on classical $H_{\infty }$ theory is presented to enhance the robustness of the design. A numerical example is given, and computer simulations are conducted to generate error and control trajectories to assess and compare the effectiveness of the presented designs.

Lidar-aided Autonomous Landing and Vision-based Taxiing for Fixed-Wing UAV

Autonomous UAV technology is limited in its ability to land safely at distinct airfields that have not been precisely surveyed and where GPS is unavailable. In this paper, we present a multi-sensor system for the automatic landing of fixed-wing UAV. The system is composed of a high precision aircraft controller, a range finder (Lidar) and a vision module used for detection and tracking of runways. The estimation of the position of the fixed-wing UAV is by using Lidar and performs gliding till flaring. Then, a federated extended Kalman filter (EKF) structure is costumed and utilizes the solutions of the IMU, GPS and Lidar as independent measurements to estimate the position of the vehicle. The framework can be used to integrate the vision solutions and enables the estimation to be smooth and robust landing. For taxiing, the neural network is used such that from live video stream from the camera trains the UAV to land precisely along the runway.

Design of Decoupling Fuzzy Logic Controller for Quadrotor UAV

This article first briefly introduces the quadrotor UAV model based on the Newton-Euler method, including kinematics model, dynamic model and force and moment model, and then proposes a decoupling fuzzy controller, which can effectively decouple the position and the orientation of the quadrotor UAV, i.e., the inner loop uses the input and output linearization method to control orientation, and the outer loop uses a fuzzy logic controller to control the position of the quadrotor UAV. Finally, we use Matlab to model the quadrotor UAV and design the decoupling fuzzy controller to control the quadrotor UAV. The results show that the tracking performance is good, which proves the feasibility of the controller algorithm.

Full-Duplex and Backhaul-Constrained UAV-Enabled Networks Using NOMA

In this paper, a full-duplex unmanned aerial vehicle-(UAV) base station is used to provide wireless communication to an area lacking a conventional terrestrial infrastructure, and its efficient deployment is investigated. More concretely, the UAV positioning and resource allocation problems are solved with the aim of minimizing the transmit power of the UAV, while serving users with their rate requirements and accounting for the backhaul limitation of the UAV. To this end, a complete solution for the optimal 3D position of the UAV, the bandwidth assignment and the transmit power distribution in the access and backhaul links is proposed that accounts for both the backhaul interference and the self-interference. When the UAV power budget is insufficient to fulfill rate requirements, non-orthogonal multiple access (NOMA) pairing is conducted to enhance system performance. Simulation results show that, when using the proposed approach, both the achieved sum rate and the percentage of satisfied users are significantly increased, when compared to a strategy that uses separate frequency bands in the access and backhaul links, as well as to a previously proposed method.

A Wind Estimation Based on Unscented Kalman Filter for Standoff Target Tracking Using a Fixed-Wing UAV

To address standoff target tracking using a fixed-wing UAV in unknown background wind, a wind estimation based on Unscented Kalman Filter (UKF) is proposed. In the paper, UAV dynamic model and target state estimation are constructed, and Lyapunov vector field guidance (LVFG) framework is introduced to achieve standoff target tracking. The unknown target state and wind dynamic model have seldom been discussed for standoff tracking in the previous research. Therefore, two typical wind dynamic models with Dryden and Davenport spectrums are constructed for the target in the high altitude or near the ground. Then, the unscented transformation is adopted to estimate the position of UAV under guidance command generated by LVFG and saturation constraint. A wind state estimation method based on UKF is proposed to improve performance of standoff tracking. Simulated and realistic ground vehicle trajectories are used to demonstrate the availability and effectiveness of the proposed method for different wind dynamic models.

UAV Positioning Mechanisms in Landing Stations: Classification and Engineering Design Review.

Landing platforms’ automation is aimed at servicing vertical take-off and landing UAVs between flights and maintaining their airworthiness. Over the last few years, different designs for the landing platforms have been proposed. This shows a strong development and establishment of automatic landing platforms with UAV positioning devices on the landing site. Positioning and safe fixation of the UAV are some of the main features of the landing platform, especially if it is mounted on a movable vehicle. This article focuses exclusively on the landing platform and its elements that provide the positioning of the UAV by affecting it during and after the landing. Both active devices and mechanisms and passive elements used for positioning are considered. This article, based on the review of recent patents and publications, gives the classification of positioning approaches used in landing stations with the analysis of the required landing precision, as well as the pros and cons of each type of approach.

Millimeter-Wave Full-Duplex UAV Relay: Joint Positioning, Beamforming, and Power Control

In this paper, a full-duplex unmanned aerial vehicle (FD-UAV) relay is employed to increase the communication capacity of millimeter-wave (mmWave) networks. Large antenna arrays are equipped at the source node (SN), destination node (DN), and FD-UAV relay to overcome the high path loss of mmWave channels and to help mitigate the self-interference at the FD-UAV relay. Specifically, we formulate a problem for maximization of the achievable rate from the SN to the DN, where the UAV position, analog beamforming, and power control are jointly optimized. Since the problem is highly non-convex and involves high-dimensional, highly coupled variable vectors, we first obtain the conditional optimal position of the FD-UAV relay for maximization of an approximate upper bound on the achievable rate in closed form, under the assumption of a line-of-sight (LoS) environment and ideal beamforming. Then, the UAV is deployed to the position which is closest to the conditional optimal position and yields LoS paths for both air-to-ground links. Subsequently, we propose an alternating interference suppression (AIS) algorithm for the joint design of the beamforming vectors and the power control variables. In each iteration, the beamforming vectors are optimized for maximization of the beamforming gains of the target signals and the successive reduction of the interference, where the optimal power control variables are obtained in closed form. Our simulation results confirm the superiority of the proposed positioning, beamforming, and power control method compared to three benchmark schemes. Furthermore, our results show that the proposed solution closely approaches a performance upper bound for mmWave FD-UAV systems.

Dynamic Contract Incentives Mechanism for Traffic Offloading in Multi-UAV Networks

Traffic offloading is considered to be a promising technology in the Unmanned Aerial Vehicles- (UAVs-) assisted cellular networks. Due to their selfishness property, UAVs may be reluctant to take part in traffic offloading without any incentive. Moreover, considering the dynamic position of UAVs and the dynamic condition of the transmission channel, it is challenging to design a long-term effective incentive mechanism for multi-UAV networks. In this work, the dynamic contract incentive approach is studied to attract UAVs to participate in traffic offloading effectively. The two-stage contract incentive method is introduced under the information symmetric scenario and the information asymmetric scenario. Considering the sufficient conditions and necessary conditions in the contract design, a sequence optimization algorithm is investigated to acquire the maximum expected utility of the base station. The simulation experiment shows that the designed two-stage dynamic contract improves the performance of traffic offloading effectively.

Pipeline Gaussian Particle Filter and Hardware Design for Attitude Estimation with UAV

This paper based on the Gaussian particle filter (GPF) deals with the attitude estimation of UAV. GPF algorithm has better estimation accuracy than the general nonlinear non-Gaussian state estimation and is usually used to improve the system’s real-time performance whose noise is specific such as Gaussian noise during the mini UAV positioning and navigation. The attitude estimation algorithm is implemented on FPGA to verify the effectiveness of the Gaussian particle filter. Simulation results have illustrated that the GPF algorithm is effective and has better real-time performance than that of the particle filter.

Study on Operation Strategy of Solar-Powered UAV Based on Longest Endurance

Aiming at the problem of energy collection and consumption of solar-powered UAV under different maneuvers, an energy collection model combining solar-powered UAV attitude and solar radiation was established. Firstly, the ASHRAE model was used as the solar radiation estimation model in the flight environment. Secondly, the energy harvesting model of the solar-powered UAV is deduced by the relationship between the attitude of the UAV, the relative position of the sun and the earth. The experimental data was used to establish the energy consumption model of solar-powered UAV in different flight states. Finally, the flight time of the same mission path under different operating modes is obtained through simulation. Simulation results show that when the solar-powered UAV is hovering at a height of 3 000 meters and a radius of 50 meters, the flight time of aileron operation is 7.19% longer than power differential operation; when hovering at a radius of 170 meters, the flight time of the two operations is almost equal; when hovering at a radius of 600 meters and flying in a line, the flight time of the power differential operation is about 1.08% longer than that of the aileron operation.

Fuzzy PD controller of UAV attitude under wind interference

In this paper, for the UAV attitude angle stabilization system under constant flat wind disturbance, the fuzzy adaptive PID control theory is used to establish the equation of wind disturbance attitude angle stability system. After mastering the control rate of the system, the fuzzy controller is designed by using matlab, and the attitude angle dynamic simulation analysis was carried out. The results show that under the interference of flat wind, the longitudinal attitude of the UAV is changed by interference, and the fuzzy adaptive PD is used to control the attitude angle of the aircraft, which has faster tracking performance, smaller adjustment time than the traditional PD control. Thereby achieving better maneuverability and less steady-state error. Therefore, the fuzzy adaptive PD can better control the attitude stability of the UAV, improve the wind resistance of the UAV, and ensure the flight is safe and reliable.

UAV Positioning Based on Multi-Sensor Fusion

Real-time and stable positioning data is essential for the UAV to perform various tasks. The traditional multi-sensor data fusion algorithm needs to know the measurement noise of sensor data, and even if there are corresponding adaptive methods to estimate the noise, most methods cannot deal with time-varying noise. In addition, traditional fusion algorithms usually are complicated, causing a large amount of calculation. In this paper, a multi-sliding window classification adaptive unscented Kalman filter (MWCAUKF) method with timestamp sort updating was proposed, which can improve the accuracy and stability of positioning. This method consists of three phases. First, according to the timestamp of sensor data, the multi-sensor data are added with fusion filtering in order. Then it estimates the measurement noise of multiple sensors through multiple sliding Windows. Finally, the sensor data classification method is adopted to deal with the filter instability caused by time-varying noise. Both theoretical analysis and experimental results show that this method has a low computational cost, high accuracy, and good stability.

A Project-Oriented Course on Guidance and Control of Autonomous Aerial Vehicles

In this paper, a project oriented course on guidance and control of autonomous aerial vehicles (UAVs) is presented. The paper describes the different modules of the course and how they are addressed using the project oriented approach. The project uses a quadrotor UAV that is used as the case study during the course. In this course, the students learn how to mathematically model quadrotor UAV flight characteristics, develop feedback control algorithms to enable stable flight control and fuse sensor measurements using Kalman filter techniques to estimate the UAV position and orientation. Students develop these concepts through both simulation and interaction with UAV real measurements. Throughout the course, students build a full 6-degree-of-freedom simulation of controlled quadrotor UAV flight using MATLAB and Simulink.