基于DRL的UAV辅助海上物联网联合卸载和资源分配

针对多无人机辅助海上物联网搜救场景,为了使无人机能处理无人船卸载的更多计算任务,同时尽可能减少无人机的能量浪费,文中提出一种基于深度确定性策略梯度(DDPG)的任务感知联合卸载和资源分配算法。首先,考虑无人机可自适应调整时隙内飞行时间以及无人船卸载任务在无人机排队计算的实际情况,建立了通信模型、计算模型和能耗模型。其次,通过联合考虑卸载决策、功率分配以及无人机飞行轨迹规划和速度调整,构建最大化所有无人机平均收益的优化问题;然后将该问题转化为马尔科夫决策过程,确立了对应的状态空间、动作空间和奖励函数,并通过DDPG算法求解出最优策略。仿真结果表明,与其他基准算法相比,所提算法可以有效提高无人机的平均收益。

基于OFDM信号的UAV测控数据链通信稳定性研究

无人机数据链通信的稳定性成为制约无人机性能的重要因素之一,若能够在测控数据采集处理过程中对数据精度进行优化处理,将极大地提高数据通信稳定性。为此,本研究结合正交频分复用信号的基本原理和特点,提出了一种新的测角算法。该算法通过设置对应的列阵雷达、差波生成方法对无人机角度估计。然后在测角结果的基础上,利用卷积神经网络和应答机制对测控数据进行优化处理,进一步校正数据误差。实验结果表明:本文的设计方法在无人机测控数据链通信稳定性方面具有显著优势,能够提供更加准确有效的测控数据,为无人机操作提供有效依据。研究结果也可以为其他无线通信系统的稳定性和可靠性问题提供有益的参考和借鉴。

OFDM-UAV中继广播通信系统航迹优化方法

为了提高无人机中继广播通信系统的性能,针对频率选择性衰落信道,考虑基站到无人机以及无人机到用户的全通信链路,提出了基于正交频分复用(OFDM)技术的无人机中继广播通信系统航迹优化方法。首先提出了基于OFDM的无人机中继广播通信传输模型,对单用户节点中断概率和系统平均中断概率进行理论分析并给出近似计算公式;然后以平均中断概率最小化和最大用户中断概率最小化为优化准则,提出无人机的航迹优化方法并分析了各因素对系统中断性能的影响;最后通过计算机仿真,证明了所提航迹优化方法正确有效。研究表明:在多径信道下,基于OFDM的无人机中继广播通信系统能有效克服频率选择性衰落,且译码转发方式下的连通性优于放大转发方式;基于平均中断概率最小化准则得到的系统中断性能优于最大用户中断概率最小化准则。

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.

Comparisons of passive microwave remote sensing sea ice concentrations with ship-based visual observations during the CHINARE Arctic summer cruises of 2010–2018

In order to apply satellite data to guiding navigation in the Arctic more effectively,the sea ice concentrations(SIC)derived from passive microwave(PM)products were compared with ship-based visual observations(OBS)collected during the Chinese National Arctic Research Expeditions(CHINARE).A total of 3667 observations were collected in the Arctic summers of 2010,2012,2014,2016,and 2018.PM SIC were derived from the NASA-Team(NT),Bootstrap(BT)and Climate Data Record(CDR)algorithms based on the SSMIS sensor,as well as the BT,enhanced NASA-Team(NT2)and ARTIST Sea Ice(ASI)algorithms based on AMSR-E/AMSR-2 sensors.The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons.The correlation coefficients(CC),biases and root mean square deviations(RMSD)between PM SIC and OBS SIC were compared in terms of the overall trend,and under mild/normal/severe ice conditions.Using the OBS data,the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness.Our results show that CC values range from 0.89(AMSR-E/AMSR-2 NT2)to 0.95(SSMIS NT),biases range from−3.96%(SSMIS NT)to 12.05%(AMSR-E/AMSR-2 NT2),and RMSD values range from 10.81%(SSMIS NT)to 20.15%(AMSR-E/AMSR-2 NT2).Floe size has a significant influence on the SIC retrievals of the PM products,and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions.Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products.Overall,the best(worst)agreement occurs between OBS SIC and SSMIS NT(AMSR-E/AMSR-2 NT2)SIC in the Arctic summer.

基于UAV成像技术的‘高原红’海棠花期物候监测研究

以‘高原红’海棠(Malus‘Prairifire’)为试材,基于UAV成像技术采集物候期内图像样本并提取色彩参数值(CIE L^(*)a^(*)b^(*)),计算不同采样点及不同物候期的色差值(△E),通过对比△E的变化情况实现海棠物候的动态监测,为实时掌握海棠物候动态变化情况,适时作出调整策略提供技术参考,同时也为开展花期物候调控相关研究提供参考依据。结果表明:1)利用Photoshop提取航拍图像色彩参数,并基于物候色彩参数进行聚类分析,将其物候划分为萌芽期、展叶期、蕾期、初花期、盛花期、末花期、落叶期、结果期、休眠期,建立了‘高原红’海棠物候色彩参数库;2)计算‘高原红’海棠各物候期内色差△E_(1)及不同物候期间色差△E_(2)范围,并基于各物候期内最大色差△E_(1max)′及不同物候期间最小色差△E_(2min)′,建立了基于色差参数的物候判别方法;3)随机提取了2个日期(3月22日、4月10日)的‘高原红’海棠物候图像色彩参数,通过聚类分析判定2个日期物候图分别处于蕾期和盛花期,结果与实际相符;2个日期物候图间色差(△E_(2)′=21.8)大于蕾期至初花期最小色差(△E_(2min)=9.6),表明此时物候期跨度较大,结果与实际相符。综上,该方法可便捷高效判断海棠花期等物候期情况。

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。

通信约束下UAV集群协同拦截任务分配算法

针对多无人机协同拦截多机动目标的任务分配问题,同时考虑到真实战场环境中存在的通信约束以及探测范围约束条件,本文提出了分步一致性拍卖算法(SCBAA)。首先,对真实战场环境中存在的通信约束以及探测范围约束等问题进行了描述分析,构建了多无人机协同拦截任务分配模型,设计了综合效能函数以及相应约束条件。其次,为解决多无人机协同打击单一目标的不平衡任务分配以及冲突消解问题,将原任务分配过程分为主要任务分配以及次要任务分配两部分,通过多次拍卖以及冲突消解实现多无人机对单一目标的任务分配。仿真结果表明,该算法可有效解决通信约束条件下的分布式多无人机协同拦截问题,并适应动态环境中任务分配对实时性的要求。

基于UAV勘察与层次分析法的安阳许家沟露天矿山地质风险评价

矿产资源是社会经济发展的重要基础,在社会发展与生态建设统筹的背景下,矿山地质风险防治与生态环境修复成为矿业研究的焦点。矿山地质风险的分类和量化是矿山地质风险防治和修复的基础,有利于因地制宜制定治理措施。本文基于UAV勘察和层次分析法对安阳许家沟露天矿山群地质风险进行了评价,根据勘察结果总结出边坡崩塌、矿坑坍塌和水土流失3个主要地质风险,各风险层选取4个因素,共12个评价指标,并利用ArcGIS栅格计算模块对选取的12个评价因素进行叠加分析。研究结果显示:高度风险区、显著风险区、一般风险区和稍有风险区的面积分别为13500 m^(2)、39000 m^(2)、26750 m^(2)、45750 m^(2),分别占研究区总面积的10.8%、31.2%、21.4%、36.6%;综合矿山地质风险排序为河西胜利II区<豫隆I区<豫安III区;从豫隆I区到豫安III区,随着研究子区面积的减少,边坡崩塌风险贡献率逐渐降低,矿坑坍塌风险和水土流失风险逐渐升高。研究结果表明,边坡崩塌评分与矿山地质评分呈现双峰态势,而矿坑坍塌风险和水土流失风险呈单峰态势。本文提出危岩清除、矿渣回填、边坡修整、客土恢复林地和耕地、养护治理等治理措施,从而降低矿山地质风险,修复矿山生态。

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

UAV倾斜摄影在革命旧址保护中的应用

针对革命旧址保护工作的严峻性,以及在获取革命旧址测区数据中传统测绘方法工作效率低、难以全面获取数据和无法获得高精度三维立体数据等问题,基于UAV倾斜摄影测量技术,以红军长征旧址为研究对象,通过外业数据采集,利用SIFT算法提取匹配影像特征点,再利用SfM-MVS算法构建三维立体场景,采集三维立体场景中的检查点数据。结果表明,三维立体场景满足大比例尺数字地形图成图规范要求;分析线元素精度可知,三维立体场景数据测量结果可达厘米级,满足革命旧址保护中对数据精度的要求;在革命旧址保护工作中还可提供多种基础数据,为今后革命旧址保护工作提供了一种高效获取高精度数据的参考方法。

Quantifying glacier surging and associated lake dynamics in Amu Darya river basin using UAV and remote sensing data

Glaciers in the Pamir region are experiencing rapid melting and receding due to climate change,which has a significant implication for the Amu Darya river basin.Predominantly,surging glaciers,which undergo unpredictable advances,are potentially leading to the obstruction of high-altitude river channels and also glacial lake outburst floods.decrease of-703.5±30.0 m.There is a substantial increase in the number(from 19 to 75)and area(from 4889.7±0.6 m2 to 15345.5±0.6 m2)of RGS lakes along with supra-glacier ponds based on a comparison of ArcGIS base map in 2011 and high-resolution UAV data in 2023.For M glacier,number of lakes increased from 4 to 22 but the lake area declined from 10715.2±0.6 to 365.6±0.6 m2.It was noted that the largest lake in 2011 with an area of 10406.4±0.6 m2 at the southeastern portion of the glacier was not observed in 2023 due to outburst.Both the glaciers have substantially impacted the river flow(Abdukahor river)by obstructing a significant proportion of river channel in recent years and might cause outburst floods.These findings enhance the understanding of glacier dynamics and their impacts on the surrounding areas,emphasizing the urgent need for continued monitoring and appropriate management strategies,with a specific focus on surging glaciers and the associated risks.

Enhancing Dense Small Object Detection in UAV Images Based on Hybrid Transformer

Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.

The signature analysis of summer Antarctic sea-ice distribution by ship-based sea-ice observation

Based on the Chinese 19th National Antarctic Research Expedition,we carried out ship-based Antarctic sea-ice observa-tion on icebreaker Xue Long using Antarctic sea-ice process and climate (ASPeCt) criteria during austral summer.Sea-ice distribution data were obtained along nearly 6,500 km of the ship’s track.The measurement parameters included sea-ice thickness,sea-ice concentration,snow thickness,and floe size.Analysis showed the presence of the large spatial varia-tions of the observed sea-ice characteristics.Sea-ice concentration varied between 0 and 80 percent and reached its peak value in Weddell Sea because of the specific dynamical process affecting in summer sea-ice melting.There are large areas of open water along the study section.Sea ice and the upper snow thickness of the section varied between 10 cm and 210 cm and 2 cm and 80 cm,respectively,and each reaches its peak values near Amery ice shelf.The floe size varied from less than 10 cm and the maximum of more than 2,000 km along the section.

Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response

Timely acquisition of rescue target information is critical for emergency response after a flood disaster.Unmanned Aerial Vehicles(UAVs)equipped with remote sensing capabilities offer distinct advantages,including high-resolution imagery and exceptional mobility,making them well suited for monitoring flood extent and identifying rescue targets during floods.However,there are some challenges in interpreting rescue information in real time from flood images captured by UAVs,such as the complexity of the scenarios of UAV images,the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform.Thus,we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets(i.e.,pedestrians and vehicles trapped by floods).The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model.The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer.Our experimental results demonstrate that the Intersection over Union(IoU)for flood water extraction reaches an impressive 80%,and the IoU for real-time flood water extraction stands at a commendable 76.4%.The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue.

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.

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.

RIS辅助的UAV与用户协同缓存策略

研究了智能反射面(RIS)和缓存辅助的无人机(UAV)中继通信系统方案,通过在UAV与用户之间搭建RIS反射信号,改善信道环境;在UAV与用户设备上部署缓存设备,预先存储热点内容,减轻无线回程链路的压力;以最大化用户服务成功概率为优化目标,建立缓存容量受限约束下的UAV与用户协同缓存放置策略优化模型,针对该非线性连续非凸约束优化问题,提出基于鲸鱼优化算法(WOA)的求解方法.仿真实验结果表明,使用RIS可以有效降低UAV通信中断概率,基于WOA的UAV与用户协同缓存最优放置策略优于现有其他两种缓存策略,能有效提高缓存命中概率,从而提高用户服务成功概率.

对抗条件下基于SAC-Lagrangian的UAV智能规划

无人机因其低成本、可消耗、分布部署、敏捷灵活的优势,在多个民用领域大放异彩。但受其智能化程度限制,如何在复杂对抗条件下自主安全完成任务仍存在巨大挑战。针对目前无人机任务规划存在的智能性和安全性问题,提出一种基于安全强化学习算法的无人机智能规划方法(SAC-Lagrangian)。考虑了雷达威胁、禁飞区安全约束和地导对抗条件,将任务规划问题建模为约束马尔可夫决策过程(CMDP),通过拉格朗日乘子法变为对偶问题,采用最大熵柔性行动者-评论家(SAC)算法近似求解最优策略,保证了智能体在遵守安全约束条件下最大化期望回报。仿真结果表明,与其他基线算法相比,所提方法能在保证任务性能的同时确保安全性,适应动态变化的场景,任务完成率达到96%,因此,具有高效、鲁棒和安全的优势。

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.