Global Navigation Satellite Systems typically perform poorly in urban environments, where the likelihood of line-of-sight conditions between devices and satellites is low. Therefore, alternative location methods are required to achieve good accuracy. We present LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task, which is able to estimate the position of a user from the received signal strength (RSS) of a small number of Base Stations (BS). Using estimations of pathloss radio maps of the BSs and the RSS measurements of the users to be localized, 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 generating RSS fingerprints of each specific area where the localization task is performed 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：全球导航卫星系统在城市环境中通常表现不佳，因为设备与卫星之间视线条件的可能性较低。因此，需要采用其他定位方法来实现良好的精度。我们提出LocUNet：一种用于定位任务的卷积端到端训练神经网络（NN），它能够根据少数基站（BS）的接收信号强度（RSS）估计用户位置。利用对基站路径损耗无线电地图的估计以及待定位用户的RSS测量值，LocUNet能够以最先进的精度定位用户，并且对无线电地图估计中的不准确性具有高鲁棒性。所提出的方法无需生成执行定位任务的每个特定区域的RSS指纹，并且适用于实时应用。此外，我们提出了两个新颖的数据集，这些数据集允许在现实城市环境中对RSS和到达时间（ToA）方法进行数值评估，并公开发布供研究社区使用。通过使用这些数据集，我们还对密集城市场景中最先进的基于RSS和ToA的方法进行了公平比较，并在数值上表明LocUNet优于所有比较方法。