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雷达卡
摘要翻译：
本文利用第五代(5G)网络中工作在毫米波(mmW)频段的多天线设备的三维波束形成特性，对用户进行精确定位和跟踪。考虑用户位置的顺序估计，提出了一种基于参考信号接收功率(RSRP)测量的两级扩展卡尔曼滤波器(EKF)。具体地说，波束成形下行链路(DL)参考信号(RS)由多个基站(BSs)发送，并由用户设备N(UE)使用接收波束成形来测量。这样得到的BRSRP测量结果被反馈到BS，在BS，相应的出发方向被一个新的EKF依次估计。来自多个BSs的这种角度估计随后借助于基于角度的EKF在中心实体上融合成UE的3D位置估计。所提出的定位方案是可扩展的，因为计算负担在不同的网络实体之间分担，即发送/接收点(TRP)和5G-NR节点B(gNB)，并且可以用当前为5G指定的信令来完成。基于METIS Madrid地图，我们通过一个详细的光线跟踪传播模型，在一个实际的5G室外部署中评估了该算法的性能。系统工作在39 GHz时的数值结果表明，在未来毫米波5G网络中，亚米级的三维定位精度是可以实现的。
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英文标题：
《User Positioning in mmW 5G Networks using Beam-RSRP Measurements and
Kalman Filtering》
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作者：
Elizaveta Rastorgueva-Foi, M\'ario Costa, Mike Koivisto, Kari
Lepp\"anen, Mikko Valkama
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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 exploit the 3D-beamforming features of multiantenna equipment employed in fifth generation (5G) networks, operating in the millimeter wave (mmW) band, for accurate positioning and tracking of users. We consider sequential estimation of users' positions, and propose a two-stage extended Kalman filter (EKF) that is based on reference signal received power (RSRP) measurements. In particular, beamformed downlink (DL) reference signals (RS) are transmitted by multiple base stations (BSs) and measured by user equipmentn(UE) employing receive beamforming. The so-obtained BRSRP measurements are fed back to the BS where the corresponding direction-of-departure are sequentially estimated by a novel EKF. Such angle estimates from multiple BSs are subsequently fused on a central entity into 3D position estimates of UE by means of an angle-based EKF. The proposed positioning scheme is scalable since the computational burden is shared among different network entities, namely transmission/reception points (TRPs) and 5G-NR Node B (gNB), and may be accomplished with the signalling currently specified for 5G. We assess the performance of the proposed algorithm on a realistic outdoor 5G deployment with a detailed ray tracing propagation model based on the METIS Madrid map. Numerical results with a system operating at 39 GHz show that sub-meter 3D positioning accuracy is achievable in future mmW 5G networks.
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PDF链接：
https://arxiv.org/pdf/1803.09478
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