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.

A landslide monitoring method using data from unmanned aerial vehicle and terrestrial laser scanning with insufficient and inaccurate ground control points

Non-contact remote sensing techniques,such as terrestrial laser scanning(TLS)and unmanned aerial vehicle(UAV)photogrammetry,have been globally applied for landslide monitoring in high and steep mountainous areas.These techniques acquire terrain data and enable ground deformation monitoring.However,practical application of these technologies still faces many difficulties due to complex terrain,limited access and dense vegetation.For instance,monitoring high and steep slopes can obstruct the TLS sightline,and the accuracy of the UAV model may be compromised by absence of ground control points(GCPs).This paper proposes a TLS-and UAV-based method for monitoring landslide deformation in high mountain valleys using traditional real-time kinematics(RTK)-based control points(RCPs),low-precision TLS-based control points(TCPs)and assumed control points(ACPs)to achieve high-precision surface deformation analysis under obstructed vision and impassable conditions.The effects of GCP accuracy,GCP quantity and automatic tie point(ATP)quantity on the accuracy of UAV modeling and surface deformation analysis were comprehensively analyzed.The results show that,the proposed method allows for the monitoring accuracy of landslides to exceed the accuracy of the GCPs themselves by adding additional low-accuracy GCPs.The proposed method was implemented for monitoring the Xinhua landslide in Baoxing County,China,and was validated against data from multiple sources.

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.

Ground target localization of unmanned aerial vehicle based on scene matching

In order to improve target localization precision,accuracy,execution efficiency,and application range of the unmanned aerial vehicle(UAV)based on scene matching,a ground target localization method for unmanned aerial vehicle based on scene matching(GTLUAVSM)is proposed.The sugges-ted approach entails completing scene matching through a feature matching algorithm.Then,multi-sensor registration is optimized by robust estimation based on homologous registration.Finally,basemap generation and model solution are utilized to improve basemap correspondence and accom-plish aerial image positioning.Theoretical evidence and experimental verification demonstrate that GTLUAVSM can improve localization accuracy,speed,and precision while minimizing reliance on task equipment.

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.

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。

Review of Effective Vegetation Mapping Using the UAV (Unmanned Aerial Vehicle) Method

We tried more precise mapping of vegetation using UAV?(unmanned aerial vehicle), as a new method of creating vegetation maps, and we?objected to be clearly the efficient mapping of vegetation using the UAV method by comparing vegetation maps created by analysing aerial photographs taken by a UAV and an aircraft (manned flight). The aerial photography using UAV was conducted in the Niida River estuary (the secondary river flowing into Minamisoma City in Fukushima Prefecture, Japan). The photography period was in August 2013. We analysed the aerial photographs using ArcGis 9 (Esri Japan Corporation, Tokyo, Japan). The aerial photographs of the main plant communities (Phragmites australis,?Typha domingensis, and?Miscanthus sacchariflorus) taken by the UAV could clearly discriminate each plant community at the 1/50 scale. Moreover, it could clearly discriminate the shape of a plant at the 1/10 scale. We compared the vegetation maps by analysing the aerial photos taken by a UAV (2013 shooting) and an aircraft (2011 shooting). As a result, the vegetation map created by the UAV method could clearly discriminate community distributions. We conclude that vegetation surveys using UAV are possible and are capable of a highly precise community division in places where field reconnaissance is difficult. The UAV method is effective and will contribute to the improvement of research methods in the future;this method may reduce research costs associated with a reduction in field survey days and man-power.

Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

Cooperative UAV search strategy based on DMPC-AACO algorithm in restricted communication scenarios

Improvement of integrated battlefield situational awareness in complex environments involving dynamic factors such as restricted communications and electromagnetic interference(EMI)has become a contentious research problem.In certain mission environments,due to the impact of many interference sources on real-time communication or mission requirements such as the need to implement communication regulations,the mission stages are represented as a dynamic combination of several communication-available and communication-unavailable stages.Furthermore,the data interaction between unmanned aerial vehicles(UAVs)can only be performed in specific communication-available stages.Traditional cooperative search algorithms cannot handle such situations well.To solve this problem,this study constructed a distributed model predictive control(DMPC)architecture for a collaborative control of UAVs and used the Voronoi diagram generation method to re-plan the search areas of all UAVs in real time to avoid repetition of search areas and UAV collisions while improving the search efficiency and safety factor.An attention mechanism ant-colony optimization(AACO)algorithm is proposed for UAV search-control decision planning.The search strategy is adaptively updated by introducing an attention mechanism for regular instruction information,a priori information,and emergent information of the mission to satisfy different search expectations to the maximum extent.Simulation results show that the proposed algorithm achieves better search performance than traditional algorithms in restricted communication constraint scenarios.

Cooperative Anti-Jamming and Interference Mitigation for UAV Networks: A Local Altruistic Game Approach

To improve the anti-jamming and interference mitigation ability of the UAV-aided communication systems, this paper investigates the channel selection optimization problem in face of both internal mutual interference and external malicious jamming. A cooperative anti-jamming and interference mitigation method based on local altruistic is proposed to optimize UAVs’ channel selection. Specifically, a Stackelberg game is modeled to formulate the confrontation relationship between UAVs and the jammer. A local altruistic game is modeled with each UAV considering the utilities of both itself and other UAVs. A distributed cooperative anti-jamming and interference mitigation algorithm is proposed to obtain the Stackelberg equilibrium. Finally, the convergence of the proposed algorithm and the impact of the transmission power on the system loss value are analyzed, and the anti-jamming performance of the proposed algorithm can be improved by around 64% compared with the existing algorithms.

基于多密度流聚类的UAV-NOMA用户分簇与功率分配算法

针对无人机(Unmanned Aerial Vehicle,UAV)辅助非正交多址(Non-Orthogonal Multiple Access,NOMA)下行通信系统,提出了最大化和速率的用户动态分簇与功率分配方案.考虑用户服务质量与UAV位置约束,建立了和速率最大化的优化问题.由于目标函数的非凸性,将原问题解耦为三个子问题,分别优化UAV位置部署与用户连接、用户动态分簇、功率分配以提高系统性能.首先,基于K-means算法设计了UAV位置部署与用户连接方案,以减小路损为目的确定UAV最佳部署位置,同时选择其服务的最优用户群;其次,改进多密度流聚类(Multi-Density Stream Clustering, MDSC)算法,提出了单UAV下用户静态与动态分簇方案,静态分簇方案可自适应平衡簇数与簇用户数,并获得较大的簇内用户信道增益差异,动态分簇方案则针对用户移动属性,制定了即时更新策略;最后,使用分式规划(Fractional Programming,FP)二次变换的方法,引入辅助变量将原非凸问题变换为凸问题,交替优化辅助变量与功率分配因子,获得原非凸问题的次优解.仿真结果表明,与其他算法相比,本文分簇方案能获得更大的簇内信道差异与更小的簇内用户数标准差,同时用户系统性能也获得了显著提升.

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.

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.

High Spatial Resolution Mapping of Dykes Using Unmanned Aerial Vehicle(UAV) Photogrammetry: New Insights On Emplacement Processes

Remote sensing has played a pivotal role in our understanding of the geometry of dykes and dyke swarms on Earth,Venus and Mars(West and Ernst,1991;Mege and Masson,1995;Ernst et al.,2005).Since the 1970’s

Heat transfer and temperature evolution in underground mininginduced overburden fracture and ground fissures: Optimal time window of UAV infrared monitoring

Heat transfer and temperature evolution in overburden fracture and ground fissures are one of the essential topics for the identification of ground fissures via unmanned aerial vehicle(UAV) infrared imager. In this study, discrete element software UDEC was employed to investigate the overburden fracture field under different mining conditions. Multiphysics software COMSOL were employed to investigate heat transfer and temperature evolution of overburden fracture and ground fissures under the influence of mining condition, fissure depth, fissure width, and month alternation. The UAV infrared field measurements also provided a calibration for numerical simulation. The results showed that for ground fissures connected to underground goaf(Fissure Ⅰ), the temperature difference increased with larger mining height and shallow buried depth. In addition, Fissure Ⅰ located in the boundary of the goaf have a greater temperature difference and is easier to be identified than fissures located above the mining goaf. For ground fissures having no connection to underground goaf(Fissure Ⅱ), the heat transfer is affected by the internal resistance of the overlying strata fracture when the depth of Fissure Ⅱ is greater than10 m, the temperature of Fissure Ⅱ gradually equals to the ground temperature as the fissures’ depth increases, and the fissures are difficult to be identified. The identification effect is most obvious for fissures larger than 16 cm under the same depth. In spring and summer, UAV infrared identification of mining fissures should be carried out during nighttime. This study provides the basis for the optimal time and season for the UAV infrared identification of different types of mining ground fissures.

Multi-UAVs Collaborative Path Planning in the Cramped Environment

Due to its flexibility and complementarity, the multiUAVs system is well adapted to complex and cramped workspaces, with great application potential in the search and rescue(SAR) and indoor goods delivery fields. However, safe and effective path planning of multiple unmanned aerial vehicles(UAVs)in the cramped environment is always challenging: conflicts with each other are frequent because of high-density flight paths, collision probability increases because of space constraints, and the search space increases significantly, including time scale, 3D scale and model scale. Thus, this paper proposes a hierarchical collaborative planning framework with a conflict avoidance module at the high level and a path generation module at the low level. The enhanced conflict-base search(ECBS) in our framework is improved to handle the conflicts in the global path planning and avoid the occurrence of local deadlock. And both the collision and kinematic models of UAVs are considered to improve path smoothness and flight safety. Moreover, we specifically designed and published the cramped environment test set containing various unique obstacles to evaluating our framework performance thoroughly. Experiments are carried out relying on Rviz, with multiple flight missions: random, opposite, and staggered, which showed that the proposed method can generate smooth cooperative paths without conflict for at least 60 UAVs in a few minutes.The benchmark and source code are released in https://github.com/inin-xingtian/multi-UAVs-path-planner.

Estimation and verification of green tide biomass based on UAV remote sensing

Since 2007,the Yellow Sea green tide has broken out every summer,causing great harm to the environment and society.Although satellite remote sensing(RS)has been used in biomass research,there are several shortcomings,such as mixed pixels,atmospheric interference,and difficult field validation.The biomass of green tide has been lacking a high-precision estimation method.In this study,high-resolution unmanned aerial vehicle(UAV)RS was used to quantitatively map the biomass of green tides.By utilizing experimental data from previous studies,a robust relationship was established to link biomass to the red-green-blue floating algae index(RGB-FAI).Then,the lab-based model for green tide biomass from visible images taken by the UAV camera was developed and validated by field measurements.Re sults show that the accurate and cost-effective method is able to estimate the green tide biomass and its changes in given local waters of the near and far seas.The study provided an effective complement to the traditional satellite RS,as well as high-precision quantitative techniques for decision-making in disaster management.

UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience

Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

Energy-efficient joint UAV secure communication and 3D trajectory optimization assisted by reconfigurable intelligent surfaces in the presence of eavesdroppers

We consider a scenario where an unmanned aerial vehicle(UAV),a typical unmanned aerial system(UAS),transmits confidential data to a moving ground target in the presence of multiple eavesdroppers.Multiple friendly reconfigurable intelligent surfaces(RISs) help to secure the UAV-target communication and improve the energy efficiency of the UAV.We formulate an optimization problem to minimize the energy consumption of the UAV,subject to the mobility constraint of the UAV and that the achievable secrecy rate at the target is over a given threshold.We present an online planning method following the framework of model predictive control(MPC) to jointly optimize the motion of the UAV and the configurations of the RISs.The effectiveness of the proposed method is validated via computer simulations.

A Novel Tensor Decomposition-Based Efficient Detector for Low-Altitude Aerial Objects With Knowledge Distillation Scheme

Unmanned aerial vehicles(UAVs) have gained significant attention in practical applications, especially the low-altitude aerial(LAA) object detection imposes stringent requirements on recognition accuracy and computational resources. In this paper, the LAA images-oriented tensor decomposition and knowledge distillation-based network(TDKD-Net) is proposed,where the TT-format TD(tensor decomposition) and equalweighted response-based KD(knowledge distillation) methods are designed to minimize redundant parameters while ensuring comparable performance. Moreover, some robust network structures are developed, including the small object detection head and the dual-domain attention mechanism, which enable the model to leverage the learned knowledge from small-scale targets and selectively focus on salient features. Considering the imbalance of bounding box regression samples and the inaccuracy of regression geometric factors, the focal and efficient IoU(intersection of union) loss with optimal transport assignment(F-EIoU-OTA)mechanism is proposed to improve the detection accuracy. The proposed TDKD-Net is comprehensively evaluated through extensive experiments, and the results have demonstrated the effectiveness and superiority of the developed methods in comparison to other advanced detection algorithms, which also present high generalization and strong robustness. As a resource-efficient precise network, the complex detection of small and occluded LAA objects is also well addressed by TDKD-Net, which provides useful insights on handling imbalanced issues and realizing domain adaptation.