摘要翻译：
本报告重点研究了三个方面：第一是接收信号强度指示技术、波达方向技术，并将RSS和DOA两种算法相结合，以构建一种混合的、更鲁棒的算法。在接收信号强度(RSS)中，利用三边测量估计未知节点位置。本报告考察了不同估计器的性能，如最小二乘、加权最小二乘和Huber稳健性，以获得最稳健的性能。在波达方向(DOA)方法中，利用多重信号分类(MUSIC)和根-音乐（Root-MUSIC）以及利用旋转不变技术(ESPRIT)估计信号参数来进行估计。我们研究了利用不同天线几何形状的多种信号场景，包括均匀线阵(ULA)和均匀圆阵(UCA)。特别关注信号成为空间相关（或相干）的多径场景。这需要使用预处理技术，包括相位模式激励(PME)、空间平滑(SS)和Toeplitz。通过对RSS和DOA的各种组合进行探索、仿真和分析，进一步改进了现有的定位技术。这导致了两个主要贡献：第一个贡献是基于UCA的RSS/DOA组合方法，它具有同时检测不相关和相干信号的能力。第二个主要贡献是基于UCA的Root-Music/Toepltiz组合方法，它在增加检测信号数量和减少计算负载方面优于其他技术。
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英文标题：
《Accurate and Robust Localization Techniques for Wireless Sensor Networks》
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作者：
Mohamed AlHajri, Abdulrahman Goian, Muna Darweesh, Rashid AlMemari,
  Raed Shubair, Luis Weruaga, Ahmed AlTunaiji
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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 report focuses on three areas in particular: the first is the Received Signal Strength indicator technique, Direction of Arrival technique, and the integration of two algorithms, RSS and DOA, in order to build a hybrid, more robust algorithms.   In the Received Signal Strength (RSS), the unknown node location is estimated using trilateration. This report examines the performance of different estimators such as Least Square, Weighted Least Square, and Huber robustness in order to obtain the most robust performance.   In the direction of arrival (DOA) method, the estimation is carried out using Multiple Signal Classification (MUSIC), Root-MUSIC, and Estimation of Signal Parameters Via Rotational Invariance Technique (ESPRIT) algorithms. We investigate multiple signal scenarios utilizing various antenna geometries, which includes uniform linear array (ULA) and uniform circular array (UCA). Specific attention is given for multipath scenarios in which signals become spatially correlated (or coherent). This required the use of pre-processing techniques, which include phase mode excitation (PME), spatial smoothing (SS), and Toeplitz.   Further improvements of existing localization techniques are demonstrated through the use of a hybrid approach in which various combinations of RSS and DOA are explored, simulated, and analyzed. This has led to two major contributions: the first contribution is a combined RSS/DOA method, based on UCA, which has the tolerance of detecting both uncorrelated and coherent signals simultaneously. The second major contribution is a combined Root-MUSIC/Toepltiz method, based on UCA, which is outperforms other techniques in terms of increased number of detected signals and reduced computationally load.
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PDF链接：
https://arxiv.org/pdf/1806.05765