Global Navigation Satellite Systems typically perform poorly in urban environments, where the likelihood of line-of-sight conditions between the devices and the satellites is low, and thus alternative localization methods are required for good accuracy. We present LocUNet: A convolutional, end-to-end trained neural network for the localization task, able to estimate the position of a user from the received signal strength (RSS) from a small number of Base Stations (BSs). In the proposed method, the user to be localized simply reports the measured RSS to a central processing unit, which may be located in the cloud. Using estimations of pathloss radio maps of the BSs and the RSS measurements, LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps. The proposed method does not require pre-sampling of new environments and is suitable for real-time applications. Moreover, two novel datasets that allow for numerical evaluations of RSS and ToA methods in realistic urban environments are presented and made publicly available for the research community. By using these datasets, we also provide a fair comparison of state-of-the-art RSS and ToA-based methods in the dense urban scenario and show numerically that LocUNet outperforms all the compared methods.
翻译:全球导航卫星系统通常在城市环境中表现不佳,在城市环境中,设备与卫星之间视线条件的可能性较低,因此需要替代的本地化方法,以便准确性良好。我们介绍LocUNet:一个用于本地化任务的连续、端到端培训神经网络,能够根据从少数基地站收到的信号强度(RSS)来估计用户的位置。在拟议方法中,用户仅将测量到的RSS报告给一个中央处理单位,而中央处理单位可能位于云层中。利用BS和RSS测量的路径失传无线电地图估计,LocUNet可以将用户以最新准确性使本地化,在对无线电地图的估计中具有高度的不准确性。拟议方法并不要求预先抽样新环境,而且适合实时应用。此外,在现实城市环境中提供两个新的数据集,用于对RSS和ToA方法进行数字评估,供研究界公开查阅。在使用这些数据模型时,我们通过使用所有基于城市的精确度和数字模型的比较方法,提供公平的城市模型。