Optimal MmWave Sensor Selection for Bearing-Only Localization in Smart Environments

Evangelos Vlachos, E. Spyrou, C. Stylios, K. Berberidis
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Abstract

Nowdays, millimeter wave (mmWave) direction sensors are being used increasingly as general-purpose radars, since they can provide high-level of accuracy for a variety of situations at low-cost. Via mutliple mmWave sensors, bearing estimation can be derived to track the position of a target, while in smart environments several sensors can be deployed. In this work, we provide an optimal sensor selection technique, for choosing which sensors to activate for bearing estimation and which not. The proposed approach is decomposed into training phase, where sensor selection is performed, and operational phase, where bearing estimation is obtained. Via simulation results we evaluate the proposed approach compared with the conventional methodology of utilizing all available data streams.
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智能环境下纯方位定位的毫米波传感器优化选择
如今,毫米波(mmWave)方向传感器越来越多地被用作通用雷达,因为它们可以以低成本为各种情况提供高精确度。通过多个毫米波传感器,可以导出方位估计来跟踪目标的位置,而在智能环境中可以部署多个传感器。在这项工作中,我们提供了一种最优传感器选择技术,用于选择激活哪些传感器进行方位估计,哪些传感器不激活。该方法分为训练阶段和操作阶段,训练阶段进行传感器选择,操作阶段进行方位估计。通过仿真结果,我们将所提出的方法与利用所有可用数据流的传统方法进行了比较。
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