Received Power Based Unmanned Aerial Vehicles (UAVs) Jamming Detection and Nodes Classification Using Machine Learning

This paper presents a machine-learning method for detecting jamming UAVs and classifying nodes during jamming attacks onWireless Sensor Networks(WSNs).Jamming is a type of Denial of Service(DoS)attack and intentional interference where a malicious node transmits a high-power signal to increase noise on the receiver side to disrupt the communication channel and reduce performance significantly.To defend and prevent such attacks,the first step is to detect them.The current detection approaches use centralized techniques to detect jamming,where each node collects information and forwards it to the base station.As a result,overhead and communication costs increased.In this work,we present a jamming attack and classify nodes into different categories based on their location to the jammer by employing a single node observer.As a result,we introduced a machine learning model that uses distance ratios and power received as features to detect such attacks.Furthermore,we considered several types of jammers transmitting at different power levels to evaluate the proposed metrics using MATLAB.With a detection accuracy of 99.7%for the k-nearest neighbors(KNN)algorithm and average testing accuracy of 99.9%,the presented solution is capable of efficiently and accurately detecting jamming attacks in wireless sensor networks.

A method to interpret fracture aperture of rock slope using adaptive shape and unmanned aerial vehicle multi-angle nap-of-the-object photogrammetry

The aperture of natural rock fractures significantly affects the deformation and strength properties of rock masses,as well as the hydrodynamic properties of fractured rock masses.The conventional measurement methods are inadequate for collecting data on high-steep rock slopes in complex mountainous regions.This study establishes a high-resolution three-dimensional model of a rock slope using unmanned aerial vehicle(UAV)multi-angle nap-of-the-object photogrammetry to obtain edge feature points of fractures.Fracture opening morphology is characterized using coordinate projection and transformation.Fracture central axis is determined using vertical measuring lines,allowing for the interpretation of aperture of adaptive fracture shape.The feasibility and reliability of the new method are verified at a construction site of a railway in southeast Tibet,China.The study shows that the fracture aperture has a significant interval effect and size effect.The optimal sampling length for fractures is approximately 0.5e1 m,and the optimal aperture interpretation results can be achieved when the measuring line spacing is 1%of the sampling length.Tensile fractures in the study area generally have larger apertures than shear fractures,and their tendency to increase with slope height is also greater than that of shear fractures.The aperture of tensile fractures is generally positively correlated with their trace length,while the correlation between the aperture of shear fractures and their trace length appears to be weak.Fractures of different orientations exhibit certain differences in their distribution of aperture,but generally follow the forms of normal,log-normal,and gamma distributions.This study provides essential data support for rock and slope stability evaluation,which is of significant practical importance.

Underdetermined direction of arrival estimation with nonuniform linear motion sampling based on a small unmanned aerial vehicle platform

Uniform linear array(ULA)radars are widely used in the collision-avoidance radar systems of small unmanned aerial vehicles(UAVs).In practice,a ULA's multi-target direction of arrival(DOA)estimation performance suffers from significant performance degradation owing to the limited number of physical elements.To improve the underdetermined DOA estimation performance of a ULA radar mounted on a small UAV platform,we propose a nonuniform linear motion sampling underdetermined DOA estimation method.Using the motion of the UAV platform,the echo signal is sampled at different positions.Then,according to the concept of difference co-array,a virtual ULA with multiple array elements and a large aperture is synthesized to increase the degrees of freedom(DOFs).Through position analysis of the original and motion arrays,we propose a nonuniform linear motion sampling method based on ULA for determining the optimal DOFs.Under the condition of no increase in the aperture of the physical array,the proposed method obtains a high DOF with fewer sampling runs and greatly improves the underdetermined DOA estimation performance of ULA.The results of numerical simulations conducted herein verify the superior performance of the proposed method.

Guaranteed Cost Attitude Tracking Control for Uncertain Quadrotor Unmanned Aerial Vehicle Under Safety Constraints

In this paper,guaranteed cost attitude tracking con-trol for uncertain quadrotor unmanned aerial vehicle(QUAV)under safety constraints is studied.First,an augmented system is constructed by the tracking error system and reference system.This transformation aims to convert the tracking control prob-lem into a stabilization control problem.Then,control barrier function and disturbance attenuation function are designed to characterize the violations of safety constraints and tolerance of uncertain disturbances,and they are incorporated into the reward function as penalty items.Based on the modified reward function,the problem is simplified as the optimal regulation problem of the nominal augmented system,and a new Hamilton-Jacobi-Bellman equation is developed.Finally,critic-only rein-forcement learning algorithm with a concurrent learning tech-nique is employed to solve the Hamilton-Jacobi-Bellman equa-tion and obtain the optimal controller.The proposed algorithm can not only ensure the reward function within an upper bound in the presence of uncertain disturbances,but also enforce safety constraints.The performance of the algorithm is evaluated by the numerical simulation.

Average Secrecy Capacity of the Reconfigurable Intelligent Surface-Assisted Integrated Satellite Unmanned Aerial Vehicle Relay Networks

Integrated satellite unmanned aerial vehicle relay networks(ISUAVRNs)have become a prominent topic in recent years.This paper investigates the average secrecy capacity(ASC)for reconfigurable intelligent surface(RIS)-enabled ISUAVRNs.Especially,an eve is considered to intercept the legitimate information from the considered secrecy system.Besides,we get detailed expressions for the ASC of the regarded secrecy system with the aid of the reconfigurable intelligent.Furthermore,to gain insightful results of the major parameters on the ASC in high signalto-noise ratio regime,the approximate investigations are further gotten,which give an efficient method to value the secrecy analysis.At last,some representative computer results are obtained to prove the theoretical findings.

Optimization of Resource Allocation in Unmanned Aerial Vehicles Based on Swarm Intelligence Algorithms

Due to their adaptability,Unmanned Aerial Vehicles(UAVs)play an essential role in the Internet of Things(IoT).Using wireless power transfer(WPT)techniques,an UAV can be supplied with energy while in flight,thereby extending the lifetime of this energy-constrained device.This paper investigates the optimization of resource allocation in light of the fact that power transfer and data transmission cannot be performed simultaneously.In this paper,we propose an optimization strategy for the resource allocation of UAVs in sensor communication networks.It is a practical solution to the problem of marine sensor networks that are located far from shore and have limited power.A corresponding system model is summarized based on the scenario and existing theoretical works.The minimum throughputmaximizing object is then formulated as an optimization problem.As swarm intelligence algorithms are utilized effectively in numerous fields,this paper chose to solve the formed optimization problem using the Harris Hawks Optimization and Whale Optimization Algorithms.This paper introduces a method for translating multi-decisions into a row vector in order to adapt swarm intelligence algorithms to the problem,as joint time and energy optimization have two sets of variables.The proposed method performs well in terms of stability and duration.Finally,performance is evaluated through numerical experiments.Simulation results demonstrate that the proposed method performs admirably in the given scenario.

Optimal Deep Learning Enabled Communication System for Unmanned Aerial Vehicles

Recently,unmanned aerial vehicles(UAV)or drones are widely employed for several application areas such as surveillance,disaster management,etc.Since UAVs are limited to energy,efficient coordination between them becomes essential to optimally utilize the resources and effective communication among them and base station(BS).Therefore,clustering can be employed as an effective way of accomplishing smart communication systems among multiple UAVs.In this aspect,this paper presents a group teaching optimization algorithm with deep learning enabled smart communication system(GTOADL-SCS)technique for UAV networks.The proposed GTOADL-SCS model encompasses a two stage process namely clustering and classification.At the initial stage,the GTOADL-SCS model includes a GTOA based clustering scheme to elect cluster heads(CHs)and organize clusters.Besides,the GTOADL-SCS model develops a fitness function containing three input parameters as residual energy of UAVs,average neighoring distance,and UAV degree.For classification process,the GTOADLSCS model applies pre-trained densely connected network(DenseNet201)feature extractor with gated recurrent unit(GRU)classifier.For ensuring the enhanced performance of the GTOADL-SCS model,a widespread simulation analysis is performed and the comparative study reported the significant outcomes over the existing approaches with maximum packet delivery ratio(PDR)of 92.60%.

3D Path Optimisation of Unmanned Aerial Vehicles Using Q Learning-Controlled GWO-AOA

Unmanned Aerial Vehicles(UAVs)or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance,due to their ability to perform repetitive and tedious tasks in hazardous environments.Their increased demand created the requirement for enabling the UAVs to traverse independently through the Three Dimensional(3D)flight environment consisting of various obstacles which have been efficiently addressed by metaheuristics in past literature.However,not a single optimization algorithms can solve all kind of optimization problem effectively.Therefore,there is dire need to integrate metaheuristic for general acceptability.To address this issue,in this paper,a novel reinforcement learning controlled Grey Wolf Optimisation-Archimedes Optimisation Algorithm(QGA)has been exhaustively introduced and exhaustively validated firstly on 22 benchmark functions and then,utilized to obtain the optimum flyable path without collision for UAVs in three dimensional environment.The performance of the developed QGA has been compared against the various metaheuristics.The simulation experimental results reveal that the QGA algorithm acquire a feasible and effective flyable path more efficiently in complicated environment.

Rice Bacterial Infection Detection Using Ensemble Technique on Unmanned Aerial Vehicles Images

Establishing a system for measuring plant health and bacterial infection is critical in agriculture.Previously,the farmers themselves,who observed them with their eyes and relied on their experience in analysis,which could have been incorrect.Plant inspection can determine which plants reflect the quantity of green light and near-infrared using infrared light,both visible and eye using a drone.The goal of this study was to create algorithms for assessing bacterial infections in rice using images from unmanned aerial vehicles(UAVs)with an ensemble classification technique.Convolution neural networks in unmanned aerial vehi-cles image were used.To convey this interest,the rice’s health and bacterial infec-tion inside the photo were detected.The project entailed using pictures to identify bacterial illnesses in rice.The shape and distinct characteristics of each infection were observed.Rice symptoms were defined using machine learning and image processing techniques.Two steps of a convolution neural network based on an image from a UAV were used in this study to determine whether this area will be affected by bacteria.The proposed algorithms can be utilized to classify the types of rice deceases with an accuracy rate of 89.84 percent.

A new approach to study terrestrial yardang geomorphology based on high-resolution data acquired by unmanned aerial vehicles(UAVs): A showcase of whaleback yardangs in Qaidam Basin, NW China

Yardangs are wind-eroded ridges usually observed in arid regions on Earth and other planets. Previous geomorphology studies of terrestrial yardang fields depended on satellite data and limited fieldwork. The geometry measurements of those yardangs based on satellite data are limited to the length, the width, and the spacing between the yardangs; elevations could not be studied due to the relatively low resolution of the satellite acquired elevation data, e.g. digital elevation models(DEMs). However, the elevation information(e.g. heights of the yardang surfaces) and related information(e.g. slope) of the yardangs are critical to understanding the characteristics and evolution of these aeolian features. Here we report a novel approach, using unmanned aerial vehicles(UAVs) to generate centimeterresolution orthomosaics and DEMs for the study of whaleback yardangs in Qaidam Basin, NW China. The ultra-high-resolution data provide new insights into the geomorphology characteristics and evolution of the whaleback yardangs in Qaidam Basin. These centimeter-resolution datasets also have important potential in:(1) high accuracy estimation of erosion volume;(2) modeling in very fine scale of wind dynamics related to yardang formation;(3) detailed comparative planetary geomorphology study for Mars, Venus, and Titan.

Contour Based Path Planning with B-Spline Trajectory Generation for Unmanned Aerial Vehicles (UAVs) over Hostile Terrain

This research focuses on trajectory generation algorithms that take into account the stealthiness of autonomous UAVs;generating stealthy paths through a region laden with enemy radars. The algorithm is employed to estimate the risk cost of the navigational space and generate an optimized path based on the user-specified threshold altitude value. Thus the generated path is represented with a set of low-radar risk waypoints being the coordinates of its control points. The radar-aware path planner is then approximated using cubic B-splines by considering the least radar risk to the destination. Simulated results are presented, illustrating the potential benefits of such algorithms.

基于DPBBO算法的智慧云仓UAV盘库作业优化

针对智慧云仓货物信息量大、易出现账物不符等库存管理问题,迫切需要将无人机(unmanned aerial vehicle, UAV)和工业物联网(industrial Internet of things, IIoT)集成起来,为仓储精细化管理提供解决方案。首先,分析盘库作业数据采集与信息交互运行机制,以危险避障和数据采集为约束函数,考虑了UAV在加速、减速、匀速、转角等飞行条件下的能耗差异,并以能耗最低和时间最短为目标函数构造UAV盘库作业数学模型;然后,设计了差分迁移-分段变异生物地理学优化(differential migration-piecewise mutation-biogeography-based optimization, DPBBO)算法对上述模型进行优化解算;最后,进行了仿真实验验证。结果表明:DPBBO算法对解决该盘库作业问题的效果较优,可以提升库存抽检任务的时效性和库存管理的准确性。

一种基于密钥协商的UAV与用户的安全通信算法

针对无人机与用户间进行通信时的安全问题,提出轻量级的安全认证和密钥协商(Se-cure Authentication and Key Agreement,SAKA)算法。SAKA算法采用轻量级的加密操作,完成无人机与用户间的安全认证,并保证无人机和用户的位置不泄露。同时,为了保证无人机与用户间的安全通信,无人机与用户在通信前相互认证,并建立会话密钥。性能分析表明,SAKA算法能防御典型的安全攻击。相比于同类的认证算法,SAKA算法在运算时间和通信成本方面具有更好的性能。

UAV SfM技术在活动构造研究中的应用——以青藏高原西北部龙木错断裂为例

为探讨搭载与未搭载实时动态差分技术/动态后处理技术(RTK/PPK)模块的无人机平台通过运动恢复结构(SfM)方法处理获得的数字高程模型(DEM)数据质量差异,以及建立不同无人机平台野外数据采集和室内数据处理过程的快速流程,利用大疆经纬M 300 RTK无人机(搭载禅思L1激光雷达(LiDAR)和测绘相机)与大疆精灵4 Pro无人机(搭载可见光相机)分别对青藏高原西北部龙木错断裂上1处位错阶地进行了数据采集,获得了该处高分辨率、高精度DEM数据和数字正射影像图(DOM)数据。对比结果显示,M 300 RTK无人机平台L1负载系统获得的LiDAR和SfM地形数据精度接近,两者在约100 m的飞行高度获得的DEM数据在水平和垂直方向的均方差分别为0.135 m、0.111 m和0.201 m、0.180 m;无RTK模块的精灵4 Pro无人机获取的DEM数据虽然绝对精度较差(水平和垂直方向均方差分别为1.707 m和249.280 m),但其反映的相对地形与实际地形接近,经过地面控制点校正后精度可以达到分米级。研究表明,RTK SfM技术克服了使用地面控制点的局限性,为活动构造研究领域微地貌测量提供了更高精度、更高效率的解决方案。当对测区的绝对三维坐标要求不高,仅需相对的地形起伏时,未搭载RTK模块的无人机也能够在无地面控制点约束的情况下满足地貌位错测量基本需求。

Improved Yield Prediction of Ratoon Rice Using Unmanned Aerial Vehicle-Based Multi-Temporal Feature Method

Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision agriculture.However,the unique agronomic practice(i.e.,varied stubble height treatment)in rice ratooning could lead to inconsistent rice phenology,which had a significant impact on yield prediction of ratoon rice.Multi-temporal unmanned aerial vehicle(UAV)-based remote sensing can likely monitor ratoon rice productivity and reflect maximum yield potential across growing seasons for improving the yield prediction compared with previous methods.Thus,in this study,we explored the performance of combination of agronomic practice information(API)and single-phase,multi-spectral features[vegetation indices(VIs)and texture(Tex)features]in predicting ratoon rice yield,and developed a new UAV-based method to retrieve yield formation process by using multi-temporal features which were effective in improving yield forecasting accuracy of ratoon rice.The results showed that the integrated use of VIs,Tex and API(VIs&Tex+API)improved the accuracy of yield prediction than single-phase UAV imagery-based feature,with the panicle initiation stage being the best period for yield prediction(R^(2) as 0.732,RMSE as 0.406,RRMSE as 0.101).More importantly,compared with previous multi-temporal UAV-based methods,our proposed multi-temporal method(multi-temporal model VIs&Tex:R^(2) as 0.795,RMSE as 0.298,RRMSE as 0.072)can increase R^(2) by 0.020-0.111 and decrease RMSE by 0.020-0.080 in crop yield forecasting.This study provides an effective method for accurate pre-harvest yield prediction of ratoon rice in precision agriculture,which is of great significance to take timely means for ensuring ratoon rice production and food security.

Coati Optimization-Based Energy Efficient Routing Protocol for Unmanned Aerial Vehicle Communication

With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile communications.The difficulty stems from features other than mobile ad-hoc network(MANET),namely aerial mobility in three-dimensional space and often changing topology.In the UAV network,a single node serves as a forwarding,transmitting,and receiving node at the same time.Typically,the communication path is multi-hop,and routing significantly affects the network’s performance.A lot of effort should be invested in performance analysis for selecting the optimum routing system.With this motivation,this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication(COAER-UAVC)technique.The presented COAER-UAVC technique establishes effective routes for communication between the UAVs.It is primarily based on the coati characteristics in nature:if attacking and hunting iguanas and escaping from predators.Besides,the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay.A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC system.The experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.

Rotary unmanned aerial vehicles path planning in rough terrain based on multi-objective particle swarm optimization

This paper presents a path planning approach for rotary unmanned aerial vehicles(R-UAVs)in a known static rough terrain environment.This approach aims to find collision-free and feasible paths with minimum altitude,length and angle variable rate.First,a three-dimensional(3D)modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs.Considering the length,height and tuning angle of a path,the path planning of R-UAVs is described as a tri-objective optimization problem.Then,an improved multi-objective particle swarm optimization algorithm is developed.To render the algorithm more effective in dealing with this problem,a vibration function is introduced into the collided solutions to improve the algorithm efficiency.Meanwhile,the selection of the global best position is taken into account by the reference point method.Finally,the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine.Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths.

IRS Assisted UAV Communications against Proactive Eavesdropping in Mobile Edge Computing Networks

In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.

Trajectory Tracking of Vertical Take-off and Landing Unmanned Aerial Vehicles Based on Disturbance Rejection Control

We investigate the trajectory tracking problem of vertical take-off and landing(VTOL) unmanned aerial vehicles(UAV), and propose a practical disturbance rejection control strategy. Firstly, the nonlinear error model is established completely by the modified Rodrigues parameters, while considering dynamics of the servo actuators. Then, a hierarchical control scheme is applied to design the translational and rotational controllers based on the time-scale property of each subsystem,respectively. And the linear extended state observer and auxiliary observer are used to deal with the uncertainties and saturation.At last, global stability of the closed-loop system is analyzed based on the singular perturbation theory. Simulation results show the effectiveness of the proposed control strategy.

Distributed tracking control of unmanned aerial vehicles under wind disturbance and model uncertainty

A distributed robust method is developed for cooperative tracking control of unmanned aerial vehicles under unknown wind disturbance and model uncertainty. The communication network among vehicles is a directed graph with switching topology. Each vehicle can only share its states with its neighbors. Dynamics of the vehicles are nonlinear and affected by the wind disturbance and model uncertainty. Feedback linearization is adopted to transform the dynamics of vehicles into linear systems. To account for the wind disturbance and model uncertainty, a robust controller is designed for each vehicle such that all vehicles ultimately synchronize to the virtual leader in the three-dimensional path. It is theoretically shown that the position states of the vehicles will converge to that of the virtual leader if the communication network has a directed spanning tree rooted at the virtual leader. Furthermore, the robust controller is extended to address the formation control problem. Simulation examples are also given to illustrate the effectiveness of the proposed method. © 2016 Beijing Institute of Aerospace Information.