Recently,unmanned aerial vehicle(UAV)-aided free-space optical(FSO)communication has attracted widespread attentions.However,most of the existing research focuses on communication performance only.The authors investig...Recently,unmanned aerial vehicle(UAV)-aided free-space optical(FSO)communication has attracted widespread attentions.However,most of the existing research focuses on communication performance only.The authors investigate the integrated scheduling of communication,sensing,and control for UAV-aided FSO communication systems.Initially,a sensing-control model is established via the control theory.Moreover,an FSO communication channel model is established by considering the effects of atmospheric loss,atmospheric turbulence,geometrical loss,and angle-of-arrival fluctuation.Then,the relationship between the motion control of the UAV and radial displacement is obtained to link the control aspect and communication aspect.Assuming that the base station has instantaneous channel state information(CSI)or statistical CSI,the thresholds of the sensing-control pattern activation are designed,respectively.Finally,an integrated scheduling scheme for performing communication,sensing,and control is proposed.Numerical results indicate that,compared with conventional time-triggered scheme,the proposed integrated scheduling scheme obtains comparable communication and control performance,but reduces the sensing consumed power by 52.46%.展开更多
Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging du...Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing.展开更多
低空无人机可以扩展通信网络的覆盖范围,并增强其通信和感知功能。近年6G、低空飞行技术、低空经济发展较为迅速,通信感知一体化(Integrated Sensing and Communication,ISAC)中的低空无人机相关理论与技术成为研究热点,带来了新的机遇...低空无人机可以扩展通信网络的覆盖范围,并增强其通信和感知功能。近年6G、低空飞行技术、低空经济发展较为迅速,通信感知一体化(Integrated Sensing and Communication,ISAC)中的低空无人机相关理论与技术成为研究热点,带来了新的机遇和挑战。梳理并归纳了面向6G的ISAC低空无人机研究成果。首先介绍ISAC低空无人机领域的研究背景,然后梳理了近几年来国内外对ISAC低空无人机领域的相关研究,从波形设计、雷达成像、干扰管理、资源管理与轨迹设计、人工智能技术应用5个方面展开综述,最后对低空无人机的ISAC技术未来发展趋势及相关挑战进行展望。展开更多
随着森林资源管理逐步迈向精准化与数字化,无人机技术为智能化与自动化的森林资源样地调查提供了一种解决方案。然而,当前树冠分割边界刻画不够精细、单木材积估测精度较低的问题仍然突出,同时高精度激光雷达数据的获取成本较高,限制了...随着森林资源管理逐步迈向精准化与数字化,无人机技术为智能化与自动化的森林资源样地调查提供了一种解决方案。然而,当前树冠分割边界刻画不够精细、单木材积估测精度较低的问题仍然突出,同时高精度激光雷达数据的获取成本较高,限制了其在实际应用中的广泛推广。为提高单木材积估测的精度与效率,克服现有方法中树冠分割不精细和高精度激光雷达数据成本高的问题,该研究提出了一种基于树冠精准分割和多源特征融合的无人机单木估测方法。在此方法中,基于YOLOv11算法,结合引入ScaleEdgeExtractor(SEE)、DilatedFusion(DF)、C2BRA和GatedFPN等模块,增强了树冠边界的感知能力和多尺度特征表达能力,并构建了高精度树冠分割网络CrownSeg。在此基础上,基于树冠形态、光谱及纹理特征的多维特征融合策略,结合递进特征组合方法和加权集成学习模型构建了单木材积估测模型。结果表明,CrownSeg树冠分割算法提升了树冠边界的刻画精度,交并比(intersection over union,IoU)阈值为0.5时的平均精度(AP50)达到94.9%,较基准模型提升1.5个百分点;IoU阈值从0.5到0.95区间的平均精度(AP50-95)达到66.2%,较基准模型提升3.8个百分点。此外,多源特征融合有效强化了单木材积的预测能力,最终加权集成模型表现优异,其决定系数(R^(2))达到0.921 5,平均绝对误差(MAE)为0.0228 m^(3),平均绝对百分比误差(MAPE)为17.00%,均优于单一模型,展现出良好的模型稳定性和泛化能力,可为无人机遥感技术在精准林业中的应用提供技术参考。展开更多
文摘Recently,unmanned aerial vehicle(UAV)-aided free-space optical(FSO)communication has attracted widespread attentions.However,most of the existing research focuses on communication performance only.The authors investigate the integrated scheduling of communication,sensing,and control for UAV-aided FSO communication systems.Initially,a sensing-control model is established via the control theory.Moreover,an FSO communication channel model is established by considering the effects of atmospheric loss,atmospheric turbulence,geometrical loss,and angle-of-arrival fluctuation.Then,the relationship between the motion control of the UAV and radial displacement is obtained to link the control aspect and communication aspect.Assuming that the base station has instantaneous channel state information(CSI)or statistical CSI,the thresholds of the sensing-control pattern activation are designed,respectively.Finally,an integrated scheduling scheme for performing communication,sensing,and control is proposed.Numerical results indicate that,compared with conventional time-triggered scheme,the proposed integrated scheduling scheme obtains comparable communication and control performance,but reduces the sensing consumed power by 52.46%.
基金National Natural Science Foundation of China(Grant Nos.62005049 and 62072110)Natural Science Foundation of Fujian Province(Grant No.2020J01451).
文摘Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing.
文摘蒸散发(Evapotranspiration,ET)是作物需水量的核心组分,也是区域水资源优化配置的关键依据。本文以陕西关中宝鸡峡灌区夏玉米为研究对象,采用BP神经网络(Back propagation neural network,BPNN)、支持向量机(Support vector machine,SVM)、极限学习机(Extreme learning machine,ELM)和极致梯度提升树(eXtreme gradient boosting,XGBoost)4种机器学习算法构建无人机-卫星多源遥感数据协同校正模型,并以最优算法建立的模型校正卫星多光谱数据,实现无人机和卫星数据的尺度转换。利用校正后高精度卫星数据反演夏玉米叶面积指数(Leaf area index,LAI)与株高(Crop height,hc)为蒸散发模型提供数据输入。分别采用双作物系数法、METRIC模型及Penman-Monteith(P-M)冠层阻力模型进行夏玉米蒸散发估算,引入贝叶斯模型平均(Bayesian model averaging,BMA)实现不同生育阶段各方法/模型权重的动态分配,最终得到玉米拔节-完熟期性能稳健的蒸散发BMA融合模型。结果表明:XGBoost算法在夏玉米拔节-完熟期的B/G/R/NIR波段建模精度均为最高,四波段建模结果决定系数(Coefficient of determination,R^(2))较算法ELM高出8.43%、8.67%、6.79%和10.41%;校正后的卫星多光谱数据LAI与hc反演结果R^(2)较原始卫星数据分别平均提高97%和67.5%;BMA融合模型在夏玉米拔节-抽雄期和蜡熟-完熟期较单一最优方法/模型(METRIC模型)均方根误差(Root mean squared error,RMSE)降低39.3%~58.5%。本研究利用“协同校正-动态融合”显著提升了蒸散发遥感监测精度,可为水资源精细化管理提供理论支撑。
文摘低空无人机可以扩展通信网络的覆盖范围,并增强其通信和感知功能。近年6G、低空飞行技术、低空经济发展较为迅速,通信感知一体化(Integrated Sensing and Communication,ISAC)中的低空无人机相关理论与技术成为研究热点,带来了新的机遇和挑战。梳理并归纳了面向6G的ISAC低空无人机研究成果。首先介绍ISAC低空无人机领域的研究背景,然后梳理了近几年来国内外对ISAC低空无人机领域的相关研究,从波形设计、雷达成像、干扰管理、资源管理与轨迹设计、人工智能技术应用5个方面展开综述,最后对低空无人机的ISAC技术未来发展趋势及相关挑战进行展望。
文摘随着森林资源管理逐步迈向精准化与数字化,无人机技术为智能化与自动化的森林资源样地调查提供了一种解决方案。然而,当前树冠分割边界刻画不够精细、单木材积估测精度较低的问题仍然突出,同时高精度激光雷达数据的获取成本较高,限制了其在实际应用中的广泛推广。为提高单木材积估测的精度与效率,克服现有方法中树冠分割不精细和高精度激光雷达数据成本高的问题,该研究提出了一种基于树冠精准分割和多源特征融合的无人机单木估测方法。在此方法中,基于YOLOv11算法,结合引入ScaleEdgeExtractor(SEE)、DilatedFusion(DF)、C2BRA和GatedFPN等模块,增强了树冠边界的感知能力和多尺度特征表达能力,并构建了高精度树冠分割网络CrownSeg。在此基础上,基于树冠形态、光谱及纹理特征的多维特征融合策略,结合递进特征组合方法和加权集成学习模型构建了单木材积估测模型。结果表明,CrownSeg树冠分割算法提升了树冠边界的刻画精度,交并比(intersection over union,IoU)阈值为0.5时的平均精度(AP50)达到94.9%,较基准模型提升1.5个百分点;IoU阈值从0.5到0.95区间的平均精度(AP50-95)达到66.2%,较基准模型提升3.8个百分点。此外,多源特征融合有效强化了单木材积的预测能力,最终加权集成模型表现优异,其决定系数(R^(2))达到0.921 5,平均绝对误差(MAE)为0.0228 m^(3),平均绝对百分比误差(MAPE)为17.00%,均优于单一模型,展现出良好的模型稳定性和泛化能力,可为无人机遥感技术在精准林业中的应用提供技术参考。
