无人机无线分布式估计弹道设计 传感器网络

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摘要翻译：
在本文中，我们研究了一种无人机支持的无线传感器网络，其中无人机被派遣从分布式传感器节点收集感知数据，以估计未知参数。研究表明，为了使估计的均方误差(MSE)最小化，无人机需要从尽可能多的SNs中收集数据，并在此基础上提出了一个基于实际机动性约束的无人机弹道设计优化问题。虽然该问题是非凸的和NP-hard的，但我们证明了无人机最优航迹仅由连通线段组成。在此基础上，利用经典的旅行商问题(TSP)方法和凸优化技术，提出了一个高效的次优解。仿真结果表明，与其他基准方案相比，所提出的轨迹设计在成功收集数据的SN数方面取得了显著的性能提高。
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英文标题：
《Trajectory Design for Distributed Estimation in UAV Enabled Wireless
  Sensor Network》
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作者：
Cheng Zhan, Yong Zeng, and Rui Zhang
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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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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一级分类：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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英文摘要：
  In this paper, we study an unmanned aerial vehicle(UAV)-enabled wireless sensor network, where a UAV is dispatched to collect the sensed data from distributed sensor nodes (SNs) for estimating an unknown parameter. It is revealed that in order to minimize the mean square error (MSE) for the estimation, the UAV should collect the data from as many SNs as possible, based on which an optimization problem is formulated to design the UAV's trajectory subject to its practical mobility constraints. Although the problem is non-convex and NP-hard, we show that the optimal UAV trajectory consists of connected line segments only. With this simplification, an efficient suboptimal solution is proposed by leveraging the classic traveling salesman problem (TSP) method and applying convex optimization techniques. Simulation results show that the proposed trajectory design achieves significant performance gains in terms of the number of SNs whose data are successfully collected, as compared to other benchmark schemes.
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PDF链接：
https://arxiv.org/pdf/1805.04364

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关键词：传感器 无人机 分布式 Optimization Applications 分布式 无人机 show optimization hard