An indoor localization approach uses Wi-Fi Access Points (APs) to estimate the Direction of Arrival (DoA) of the WiFi signals. This paper demonstrates FIND, a tool for Fine INDoor localization based on a software-defined radio, which receives Wi-Fi frames in the 80 MHz band with four antennas. To the best of our knowledge, it is the first-ever prototype that extracts from such frames data in both frequency and time domains to calculate the DoA of Wi-Fi signals in real-time. Apart from other prototypes, we retrieve from frames comprehensive information that could be used to DoA estimation: all preamble fields in the time domain, Channels State Information, and signal-to-noise ratio. Using our device, we collect a dataset for comparing different algorithms estimating the angle of arrival in the same scenario. Furthermore, we propose a novel calibration method, eliminating the constant phase shift between receiving paths caused by hardware imperfections. All calibration data, as well as a gathered dataset with various DoA in an anechoic chamber and in a classroom, are provided to facilitate further research in the area of indoor localization, intelligence surfaces, and multi-user transmissions in dense deployments.
翻译:室内本地化方法使用Wi-Fi 信号的 Wi-Fi 访问点( APs) 来估计 Wi- Fi 信号的抵达方向( DoA) 。 本文展示了 SFD, 这是基于软件定义的无线电, 接收80兆赫带的 Wi- Fi 框架和四天线。 据我们所知, 这是第一个在频率和时间范围内提取这种框架数据的原型, 以实时计算Wi- Fi 信号的到达方向 。 除了其他原型外, 我们从框架中检索可用于 doA 估计的全面信息: 所有时间域的序言字段, 频道国家信息, 以及信号到音频比 。 我们使用我们的设备, 收集一套数据, 用于比较估计同一情景中到达角度的不同算法 。 此外, 我们提议一种新校准方法, 消除因硬件不完善而导致接收路径之间的恒定阶段转移 。 所有校准数据, 以及与 DoA 一起收集的多功能室和教室的数据集, 将便利在室内的中央、 传输和多路段部署 。