Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach

要約

タイトル：ラジオマップを用いたリアルタイム屋外位置特定：ディープラーニング手法によるアプローチ

要約：
– 衛星を用いた位置特定は都市環境における直視率の低さの影響を受け対策が必要。
– LocUNetは、少数のBases Station（BS）の受信信号強度（RSS）からユーザーの位置を推定する畳み込みニューラルネットワーク（NN）で、高い信頼性と精度を持つ。
– また、LocUNetは、ラジオマップの推定誤差にも強く、リアルタイムアプリケーションに適している。
– 二つの新しいデータセットも公開されたため、ステート・オブ・ジ・アートなRSSおよびToA手法と比較してLocUNetが優れていることが数値的に証明された。

要約(オリジナル)

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.

arxiv情報

提供元, 利用サービス

arxiv.jp, OpenAI