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雷达卡
摘要翻译：
提出了一种物联网（IoT）与无人机(UAV)相结合的智能技术，用于最大化网络连通性和提供所需的服务质量（QoS）。对信号强度和衰落信道条件的预测使得自适应数据传输成为可能，从而提高终端用户/设备的QoS，同时降低数据传输的功率消耗。无人机是从人类难以到达或不可能到达的区域采集数据的机器人。因此，大气动力和环境影响着无人机、物联网设备和人类在太空中的信号强度。因此，来自智能无人机的信号运动对衰减、反射、衍射、散射和阴影的影响非常敏感。利用人工神经网络，分析了人工神经网络从无人机和物理介质参数预测信号强度和信道传播的能力。此外，实验结果表明，该方法可以显著降低和增强信号的失真。
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英文标题：
《Predictive Estimation of the Optimal Signal Strength from Unmanned
Aerial Vehicle over Internet of Things Using ANN》
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作者：
S. H. Alsamhi, Ou Ma, M. S. Ansari
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
This paper proposes an intelligent technique for maximizing the network connectivity and provisioning desired quality of service (QoS) of integration of internet of things (IoT) and unmanned aerial vehicle (UAV). Prediction of the signal strength and fading channel conditions enable adaptive data transmission which turn enhances the QoS for the end users/ devices with reducing the power consumption for data transmissions. UAV is data gathering robot from the difficult or impossible area for humans to reach. Hence, Atmospheric dynamics and environment influence the signal strength during traveling in space among UAV, IoT devices, and humankind. Therefore, Signal moving from the smart UAV is sensitive to the effects of attenuation, reflection, diffraction, scattering, and shadowing. We analysis the ability ANN to predictively estimate the signal strength and channel propagation from the drone and physical medium parameters, using ANN. Moreover, the results show that the distortion of the signal can be reduced and enhanced significantly.
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PDF链接：
https://arxiv.org/pdf/1805.07614
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