无线传感器移动节点定位的鲁棒算法 网络

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摘要翻译：
新兴的无线传感器技术(WSN)的成功催生了通信领域的定位概念。事实上，随着无线传感器网络在医学、军事、交通等领域的广泛应用，人们对本地化的兴趣也越来越大。通过利用传感器终端的子集，在无线传感器网络中收集的数据可以被识别和关联，这有助于管理分布在整个网络中的节点。在文献中介绍的大多数场景中，要本地化的节点通常被认为是静态的。然而，随着我们迈向第五代移动通信，移动性方面应该得到重视。因此，这项研究的新颖性在于它能够融合机器人技术以及无线传感器网络领域，为移动节点的定位创造了一个艺术状态。具有挑战性的方面依赖于以尽可能最小化各自限制的方式合并这两个平台的能力。提出了一种将粒子滤波(PF)和到达时间差(TDOA)相结合的混合技术。仿真结果表明，该方法在精度和鲁棒性方面优于其他方法。
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英文标题：
《Robust Algorithms for Localizing Moving Nodes in Wireless Sensor
  Networks》
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作者：
Hadeel Elayan and Raed Shubair
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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：
  The vivid success of the emerging wireless sensor technology (WSN) gave rise to the notion of localization in the communications field. Indeed, the interest in localization grew further with the proliferation of the wireless sensor network applications including medicine, military as well as transport. By utilizing a subset of sensor terminals, gathered data in a WSN can be both identified and correlated which helps in managing the nodes distributed throughout the network. In most scenarios presented in the literature, the nodes to be localized are often considered static. However, as we are heading towards the 5th generation mobile communication, the aspect of mobility should be regarded. Thus, the novelty of this research relies in its ability to merge the robotics as well as WSN fields creating a state of art for the localization of moving nodes. The challenging aspect relies in the capability of merging these two platforms in a way where the limitations of each is minimized as much as possible. A hybrid technique which combines both the Particle Filter (PF) method and the Time Difference of Arrival Technique (TDOA) is presented. Simulation results indicate that the proposed approach outperforms other techniques in terms of accuracy and robustness.
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PDF链接：
https://arxiv.org/pdf/1806.11214

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关键词：传感器 Applications localization Optimization Application 网络 平台 合并 节点 relies