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Scalable real-time position detection based on overlapping neural networks

机译:基于重叠神经网络的可扩展实时位置检测

摘要

A method for real-time location detection consists of three groups of components. Mobile subjects to be tracked are equipped with wireless transceivers capable of sending and optionally for receiving data over pre-determined radio frequency (RF) band(s). Router/base station access point devices are equipped with wireless transceivers capable of sending and receiving data over pre-determined radio frequency (RF) band(s) in order to communicate with mobile units. Routers are combined into specific overlapping router groups, with each group forming a spatial sub-network. System central processing and command station(s) perform data processing and implementation of computational models that determine the mobile unit location. System deployment consists of three phases: collection of training and testing data, network training and testing, and network adaptive maintenance.
机译:实时位置检测方法由三组组件组成。待跟踪的移动对象配备了无线收发器,该收发器能够在预定的射频(RF)频段上发送数据并可选地用于接收数据。路由器/基站接入点设备配备有无线收发器,该收发器能够在预定的射频(RF)频段上发送和接收数据,以便与移动单元进行通信。路由器被组合成特定的重叠路由器组,每个组形成一个空间子网。系统中央处理和命令站执行数据处理以及确定移动单元位置的计算模型。系统部署包括三个阶段:训练和测试数据的收集,网络训练和测试以及网络自适应维护。

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