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.

Remote sensing image encryption algorithm based on novel hyperchaos and an elliptic curve cryptosystem

Remote sensing images carry crucial ground information,often involving the spatial distribution and spatiotemporal changes of surface elements.To safeguard this sensitive data,image encryption technology is essential.In this paper,a novel Fibonacci sine exponential map is designed,the hyperchaotic performance of which is particularly suitable for image encryption algorithms.An encryption algorithm tailored for handling the multi-band attributes of remote sensing images is proposed.The algorithm combines a three-dimensional synchronized scrambled diffusion operation with chaos to efficiently encrypt multiple images.Moreover,the keys are processed using an elliptic curve cryptosystem,eliminating the need for an additional channel to transmit the keys,thus enhancing security.Experimental results and algorithm analysis demonstrate that the algorithm offers strong security and high efficiency,making it suitable for remote sensing image encryption tasks.

Seamless integration of above-and undercanopy unmanned aerial vehicle laser scanning for forest investigation

Background:Current automated forest investigation is facing a dilemma over how to achieve high tree-and plotlevel completeness while maintaining a high cost and labor efficiency.This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle(UAV)that flies above and under canopies in a single operation.The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight,thus grants the access to simultaneous high completeness,high efficiency,and low cost.Results:In the experiment,an approximately 0.5 ha forest was covered in ca.10 min from takeoff to landing.The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems,which leads to a 2–4 cm RMSE of the diameter at the breast height estimates,and a 4–7 cm RMSE of the stem curve estimates.Conclusions:Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective.Thus,it is a solution to combine the advantages of the terrestrial static,the mobile,and the above-canopy UAV observations,which is a promising step forward to achieve a fully autonomous in situ forest inventory.Future studies should be aimed to further improve the platform positioning,and to automatize the UAV operation.

Optimization of the Internet of Remote Things Data Acquisition Based on Satellite UAV Integrated Network

In order to achieve dependable and efficient data acquisition and transmission in the Internet of Remote Things(IoRT),we investigate the optimization scheme of IoRT data acquisition under the unmanned aerial vehicle(UAV)-low earth orbit(LEO)satellite integrated space-air-ground network,in which the UAV acquires data from massive Internet of Things(IoT)devices in special scenarios.To combine with the actual scenario,we consider two different data types,that is,delay-sensitive data and delay-tolerant data,the transmission mode is accordingly divided into two types.For delay-sensitive data,the data will be transmitted via the LEO satellite relay to the data center(DC)in real-time.For delay-tolerant data,the UAV will store and carry the data until the acquisition is completed,and then return to DC.Due to nonconvexity and complexity of the formulated problem,a multi-dimensional optimization Rate Demand based Joint Optimization(RDJO)algorithm is proposed.The algorithm first uses successive convex approximation(SCA)technology to solve the non-convexity,and then based on the block coordinate descent(BCD)method,the data acquisition efficiency is maximized by jointly optimizing UAV deployment,the bandwidth allocation of IoRT devices,and the transmission power of the UAV.Finally,the proposed RDJO algorithm is compared with the conventional algorithms.Simulation consequences demonstrate that the efficiency of IoRT data acquisition can be greatly improved by multi-parameter optimization of the bandwidth allocation,UAV deployment and the transmission power.

Geometrical feature analysis and disaster assessment of the Xinmo landslide based on remote sensing data

At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.

Sensing and Communication Integrated Fast Neighbor Discovery for UAV Networks

In unmanned aerial vehicle(UAV)networks,the high mobility of nodes leads to frequent changes in network topology,which brings challenges to the neighbor discovery(ND)for UAV networks.Integrated sensing and communication(ISAC),as an emerging technology in 6G mobile networks,has shown great potential in improving communication performance with the assistance of sensing information.ISAC obtains the prior information about node distribution,reducing the ND time.However,the prior information obtained through ISAC may be imperfect.Hence,an ND algorithm based on reinforcement learning is proposed.The learning automaton(LA)is applied to interact with the environment and continuously adjust the probability of selecting beams to accelerate the convergence speed of ND algorithms.Besides,an efficient ND algorithm in the neighbor maintenance phase is designed,which applies the Kalman filter to predict node movement.Simulation results show that the LA-based ND algorithm reduces the ND time by up to 32%compared with the Scan-Based Algorithm(SBA),which proves the efficiency of the proposed ND algorithms.

Experimental Comparison of Direct and Indirect Haptic Aids in Support of Obstacle Avoidance for Remotely Piloted Vehicles

The sense of telepresence is known to be essential in teleoperation environments, where the operator is physically separated from the vehicle. Usually only a visual feedback is provided, but it has been shown that by extending the visual interface with haptic feedback, that is complementing the visual information through the sense of touch, the teleoperator has a better perception of information from the remote environment and its constraints. This paper focuses on a novel concept of haptic cueing for an airborne obstacle avoidance task; the novel cueing algorithm was designed to appear ＂natural＂ to the operator, and to improve the human-machine interface without directly acting on the actual aircraft commands. Two different haptic aiding concepts for obstacle avoidance support are presented： an existing and widely used system, belonging to what we called the Direct Haptic Aid （DItA） approach class, and a novel one based on the Indirect Haptic Aid （IHA） approach class. Tests with human operators show that a net improvement in terms of performance （i.e., the number of collisions） is provided by employing the 1HA haptic cue as compared to both the DHA haptic cue and/or the visual cues only. The results clearly show that the IHA philosophy is a valid alternative to the other commonly used approaches, which fall in the DHA category.

Monitoring coal fires in Datong coalfield using multi-source remote sensing data

Numerous coal fires burn underneath the Datong coalfield because of indiscriminate mining.Landsat TM/ETM,unmanned aerial vehicle（UAV）,and infrared thermal imager were employed to monitor underground coal fires in the Majiliang mining area.The thermal field distributions of this area in 2000,2002,2006,2007,and 2009 were obtained using Landsat TM/ETM.The changes in the distribution were then analyzed to approximate the locations of the coal fires.Through UAV imagery employed at a very high resolution（0.2 m）,the texture information,linear features,and brightness of the ground fissures in the coal fire area were determined.All these data were combined to build a knowledge model of determining fissures and were used to support underground coal fire detection.An infrared thermal imager was used to map the thermal field distribution of areas where coal fire is serious.Results were analyzed to identify the hot spot trend and the depth of the burning point.

Intelligent Deep Data Analytics Based Remote Sensing Scene Classification Model

Latest advancements in the integration of camera sensors paves a way for newUnmannedAerialVehicles(UAVs)applications such as analyzing geographical(spatial)variations of earth science in mitigating harmful environmental impacts and climate change.UAVs have achieved significant attention as a remote sensing environment,which captures high-resolution images from different scenes such as land,forest fire,flooding threats,road collision,landslides,and so on to enhance data analysis and decision making.Dynamic scene classification has attracted much attention in the examination of earth data captured by UAVs.This paper proposes a new multi-modal fusion based earth data classification(MMF-EDC)model.The MMF-EDC technique aims to identify the patterns that exist in the earth data and classifies them into appropriate class labels.The MMF-EDC technique involves a fusion of histogram of gradients(HOG),local binary patterns(LBP),and residual network(ResNet)models.This fusion process integrates many feature vectors and an entropy based fusion process is carried out to enhance the classification performance.In addition,the quantum artificial flora optimization(QAFO)algorithm is applied as a hyperparameter optimization technique.The AFO algorithm is inspired by the reproduction and the migration of flora helps to decide the optimal parameters of the ResNet model namely learning rate,number of hidden layers,and their number of neurons.Besides,Variational Autoencoder(VAE)based classification model is applied to assign appropriate class labels for a useful set of feature vectors.The proposedMMF-EDCmodel has been tested using UCM and WHU-RS datasets.The proposed MMFEDC model attains exhibits promising classification results on the applied remote sensing images with the accuracy of 0.989 and 0.994 on the test UCM and WHU-RS dataset respectively.

Relationship between Vegetation Index and Forest Surface Fuel Load in UAV Multispectral Remote Sensing

[Objectives]To explore the relationship between vegetation index and forest surface fuel load.[Methods]UAV multispectral remote sensing was used to obtain large-scale forest images and obtain structural data of forest surface fuel load.This experimental area was located in Gaoming District,Foshan City,Guangdong Province.The average surface fuel load of the experimental area was as high as 39.33 t/ha,and the forest surface fuel load of Pinus elliottii was the highest.[Results]The normalized difference vegetation index(NDVI)and enhanced vegetation index(EVI)had a moderately strong correlation with the forest surface fuel load.The regression model of NDVI(X)and forest surface fuel load(Y)was established:Y=-5.9354X+8.4663,and the regression model of EVI(X)and forest surface fuel load(Y)was established:Y=-5.8485X+6.7271.The study also found that the linear relationship between NDVI and surface fuel load was more significant.[Conclusions]Both NDVI and EVI have moderately strong correlations with forest surface fuel load.NDVI is moderately or strongly correlated with the surface fuel load of Pinus massoniana forest,shrub grassland,broad-leaf forest and bamboo forest,while EVI is only strongly correlated with surface fuel load of broad-leaf forest and bamboo forest.It is expected that the relationship between other vegetation indices and forest surface fuel load can be obtained by the method in this study,so as to find a more universal vegetation index for calculating surface fuel load.

Spectrum and energy efficient multi-antenna spectrum sensing for green UAV communication

Unmanned Aerial Vehicle(UAV)communication is a promising technology that provides swift and flexible ondemand wireless connectivity for devices without infrastructure support.With recent developments in UAVs,spectrum and energy efficient green UAV communication has become crucial.To deal with this issue,Spectrum Sharing Policy(SSP)is introduced to support green UAV communication.Spectrum sensing in SSP must be carefully formulated to control interference to the primary users and ground communications.In this paper,we propose spectrum sensing for opportunistic spectrum access in green UAV communication to improve the spectrum utilization efficiency.Different from most existing works,we focus on the problem of spectrum sensing of randomly arriving primary signals in the presence of non-Gaussian noise/interference.We propose a novel and improved p-norm-based spectrum sensing scheme to improve the spectrum utilization efficiency in green UAV communication.Firstly,we construct the p-norm decision statistic based on the assumption that the random arrivals of signals follow a Poisson process.Then,we analyze and derive the approximate analytical expressions of the false-alarm and detection probabilities by utilizing the central limit theorem.Simulation results illustrate the validity and superiority of the proposed scheme when the primary signals are corrupted by additive non-Gaussian noise and arrive randomly during spectrum sensing in the green UAV communication.

基于时间序列植被指数的小麦条锈病抗性等级鉴定方法

条锈病严重影响小麦产量,培育抗条锈病的小麦品种至关重要。针对传统育种中抗性鉴定手段单一、效率低的问题,该研究提出了一种通过小麦冠层植被指数的时间序列实现对条锈病不同抗性等级的高效鉴定方法。该方法利用无人机采集自然发病的育种群体小麦(共600个样本,516个基因型)冠层多时相的光谱图像,使用随机蛙跳算法和ReliefF算法筛选出6个条锈病病害严重度的敏感特征:归一化色素叶绿素指数(normalized pigment chlorophyll index,NPCI)、沃尔贝克指数(woebbecke index,WI)、叶绿素红边指数(chlorophyll index rededge,CIrededge)、绿大气抵抗植被指数(green atmospherically resistant index,GARI)、归一化差分植被指数(normalized difference vi,NDVI)、叶绿素绿指数(chlorophyll index green,CIgreen),这些敏感特征在试验群体中的时间序列符合条锈病的发病规律,验证了其作为条锈病发病严重度敏感特征的有效性;基于支持向量机(support vector machine,SVM)算法使用上述敏感特征建立条锈病病害严重度等级分类模型,在测试集的表现中,与使用未经过筛选的原始特征所建立的模型相比在精度、平均准确率、平均召回率和F1分数上分别仅下降6.2%、3.3%、2.7%、4.0%,证明了所筛选敏感特征的有效性;针对一般机器学习算法难以捕捉不同抗性等级样本之间较小的特征变化差异的问题,提出了一种从植被指数时间序列转化生成的二维图像中提取特征实现条锈病抗性等级分类的方法。将敏感特征中能够较好区分不同抗病等级的4个时间序列植被指数(NPCI、GARI、NDVI、WI),通过格拉姆角场方法生成格拉姆角和场图像,并制作成数据集,使用DenseNet121网络进行训练,以实现不同条锈病抗病等级的分类。建立的条锈病抗性等级分类模型中,由NPCI时间序列图像建立的分类模型测试效果最佳,其准确率为0.837,召回率为0.834,F1分数可达0.833,能够较好地实现对群体小麦不同品种(系)的条锈病抗性等级差异的区分,表明基于光谱植被指数时间序列的小麦条锈病抗性等级识别方法可以用于小麦抗病育种中抗性等级的鉴定,并可为其他作物的病害抗性等级鉴定提供一定的参考。

基于脑机接口与人机闭环的远程脑控无人机系统

随着现代军事战争的迅速演变,远程脑控无人机在实现战场信息获取、目标监视和战术部署方面扮演着愈发重要的角色。提出一种应用于远程脑控无人机的压缩感知控制范式和人机闭环控制算法,基于该控制范式及控制算法搭建面向军事应用场景的远程脑控无人机系统。在线实验结果表明:8名被试人员通过该脑控无人机系统进行导航任务,平均任务完成率为0.95,平均任务完成时间为100.46 s,显著优于基于人机开环控制算法的脑控无人机系统;新提出的脑控无人机系统可以应用于军事场景下的战场侦察,大幅度提高作战人员的无人机远程控制能力,拓展作战人员的战场感知范围。

多机协同自主任务规划系统研究综述

针对多机协同自主任务规划显著提升无人机任务感知效能、提高任务执行能力的现状,对其关键技术进行阐述,主要包括知识表示、通感一体化、任务调度及航迹规划等技术。针对知识表示技术,阐述基于任务环境上下文感知的知识库构建方法,进而对多域融合知识图谱构建、动态知识图谱更新与共享方法进行分析总结;针对通感一体化技术,分析了环境适变、灵活可扩的多机协同通感一体化架构,进而揭示多机协同通感一体化理论能限,阐述面向任务差异性需求的资源共享方法;针对任务调度及航迹规划技术,阐述资源适配的多机协同自主任务调度方案,并对基于动力学模型的航迹控制策略及不完美环境下的鲁棒控制机制进行分析总结。

无人机低空遥感结合YOLOv7快速评估水稻穗颈瘟抗性

为解决传统水稻稻瘟病抗性评估手段单一、效率低的问题,该研究提出一种无人机低空遥感技术结合YOLOv7模型的水稻穗颈瘟抗性鉴定方法。首先,将标注区域分割成小尺寸图像(≤1 240×1 240像素),将小尺寸图像进行旋转、缩放、平移、剪切和改变对比度处理。经数据清洗,去除分辨率过低的图像,扩充样本数量,以提高数据多样性。然后,将压缩注意力机制(squeeze-excitation attention)和可变形卷积(deformable convolution)引入YOLOv7模型,自适应调整感受野,以提升捕捉穗颈瘟病斑细粒度特征的能力。最后,构建穗颈瘟检测的YOLOv7_Neckblast模型。研究结果表明,YOLOv7_Neckblast能够有效检测穗颈瘟,计算出15个品种的穗颈瘟发病率和病害等级(1、3、5、7和9级的水稻品种分别有4、4、3、2和2个)。在交并比阈值为0.5时,YOLOv7_Neckblast平均精度均值相较于YOLOv7、FCOS和RetinaNet分别提升了4.0、6.4和5.8个百分点,召回率和F1值分别提高了至少4.0和4.0个百分点,且浮点计算数和参数量最低。与育种专家判定的实际抗性水平相比,YOLOv7_Neckblast模型对15个水稻品种的穗颈瘟抗性水平的平均评估准确率为86.67%,能较好地实现不同水稻品种穗颈瘟抗性的区分。无人机低空遥感融合人工智能技术可用于水稻黄熟期育种中穗颈瘟抗性的评估,也可为其他作物优势品种的选育提供参考。

低空监控系统的红外小目标检测方法

无人机遥感系统在检测复杂背景下红外小目标时存在虚警过高的问题,结合卷积神经网络提出一种两阶段的无人机遥感系统红外小目标检测模型。第一阶段利用Unet神经网络学习红外图像中目标的深度语义特征与浅层位置特征,同时增强红外小目标信号,并抑制背景信号。第二阶段利用Faster R-CNN对第一阶段输出的图像进行分析,检测图中红外小目标的位置与边框。在公开的无人机遥感系统红外小目标检测数据集上完成了验证实验,结果表明该模型将三个复杂背景数据集下红外小目标的检测精度分别提高了13.2、9.8与13个百分点,每秒处理的帧数分别增加了11、14与13。

基于通感融合的无人机预编码及飞行轨迹设计

无人机(UAVs)具有机动性强,低成本及易部署等特性,通过搭载通信及感知设备,支持通信与感知技术的高效资源共享,无人机可作为融合通信与传感技术的高性能空中平台。该文针对多输入多输出(MIMO)无人机使能的联合通信、感知场景,综合考虑无人机飞行能量、多天线传输及用户业务需求等限制条件,建模无人机通信、感知预编码及飞行轨迹联合优化问题为多目标优化问题,以实现通信用户最低速率最大化及目标最小发现概率最大化。由于通信用户最低速率最大化问题为非凸优化问题,难以直接求解,将原优化问题分解为通信预编码设计子问题及无人机轨迹设计子问题,并采用交替迭代法依次求解两个子问题直至算法收敛,其中,对于通信预编码设计子问题,提出一种基于迫零(ZF)算法的求解策略;对于无人机轨迹设计子问题,提出一种基于连续凸逼近(SCA)算法的求解策略。基于所得到的无人机最优轨迹,将无人机感知位置选择问题建模为加权距离和最小化问题,进而应用泛搜索算法优化确定目标感知位置,并设计基于ZF算法的通信感知预编码联合优化策略,以实现通信感知性能的联合优化。最后通过仿真验证了该文所提算法的有效性。

基于多光谱遥感和CNN的玉米地上生物量估算模型

目前玉米地上生物量(Aboveground biomass,AGB)的预测方法集中在使用从无人机图像中提取光学植被指数,通过线性模型或机器学习算法与AGB建立关系,原始图像信息损失严重,玉米生长后期的饱和效应会严重降低模型精度。针对此问题,本文收集了玉米拔节期、吐丝期和乳熟期的无人机图像和地面数据。分析了不同生育期玉米干地上生物量、鲜地上生物量与8个植被指数(Vegetation indexes,VIs)之间的相关性。分别以最优植被指数作为输入建立多层感知机(Multilayer perceptron,MLP)模型、以无人机多光谱图像作为输入建立卷积神经网络(Convolutional neural network,CNN)模型来估算玉米干地上生物量、鲜地上生物量。结果表明,基于MLP的玉米干地上生物量估算模型随着玉米生育期推进,模型的精度急剧下降,3个生长期MLP模型验证集R^(2)分别为0.65、0.23、0.32,RMSE分别为0.27、2.15、5.03 t/hm^(2)。CNN模型能够较好地克服光谱饱和问题,具有良好的精度和适用性,3个生育期验证集R^(2)分别提高27.69%、191.30%、171.88%,RMSE分别降低22.22%、38.14%、45.53%。基于MLP的玉米鲜地上生物量估算模型在玉米生长后期模型的精度同样较低,吐丝期、乳熟期验证集的R^(2)分别为0.27、0.37,RMSE分别为11.57、14.98 t/hm^(2)。CNN模型2个生育期验证集的R^(2)分别提高159.26%、129.73%,RMSE分别降低26.62%、54.01%。使用原始多光谱图像作为输入的CNN模型取得了最好的估计结果,可为玉米不同生育期的监测研究、精准管理提供指导。

露天矿无人机遥感边坡地表形变提取方法研究

针对当前露天矿边坡监测过程中存在的设备留有监测死角、点位布控缺乏依据、地质隐患解译困难、无人机(Unmanned Aerial Vehicle,UAV)影像点云重构复杂度高等问题,提出了一种基于无人机遥感的边坡地表形变提取方法。首先,通过分析UAV激光点云与影像特点构建点云序列;其次,利用融合尺度不变特征变换(Scale Invariant Feature Transform,SIFT)与圆柱形邻域搜索的改进迭代最近点(Iterative Closest Point,ICP)算法,实现点云序列的精准高效配准,提高边坡形变提取精度;最终,借助数字高程模型(Digital Elevation Model,DEM)叠加分析与可视化,精准定位边坡重点形变区域,直观提取边坡形变位置和大小,并结合正射影像图像特征进行形变区域分析与解译。以南芬露天矿为工程应用实例,研究表明:边坡形变模型标准偏差为0.032 m,对比全球定位系统-实时动态差分(Global Positioning System-Real Time Kinematic,GPS-RTK)实测形变值,形变中误差为0.012 m,能够快速实现大尺度复杂边坡地表扫描与形变提取,从而为地质灾害隐患分析、盲区边坡形变监测与地面监测设备科学布控提供技术支撑。

铁路应急场景下无人机通信感知一体化无线网络资源智能分配算法

面向恶劣自然环境下地面基础设施受损的铁路场景,该文提出一种无人机(UAV)通信感知一体化无线接入网络架构,实现对列车运行环境的实时感知及应急信息回传。考虑到无人机的续航能力有限,通过建立列车制动距离模型与无人机能耗模型,在满足信息回传通信性能与列车环境感知需求的情况下,联合调整无人机飞行速度和通信发射功率以优化无人机整体能耗。通过分析发现,该优化问题符合马尔可夫决策过程(MDP),基于此,提出一种基于深度双Q网络(DDQN)的无人机通信感知一体化无线资源智能分配算法解决上述问题。最后,该文对所提算法的收敛性能、无人机环境感知距离和无人机能耗进行了仿真实验。仿真结果显示,所提算法具有良好的收敛性能,在满足铁路应急场景环境感知及信息回传需求的同时,能够最大化无人机通信作业时长。