Effective indoor localization largely relies on the fingerprint database (model) of Received Signal Strength (RSS) in connection with Radio Frequency sources, such as the most widely used Bluetooth Low Energy (BLE) iBeacons. RSSs exhibit significant random variations in both the spatial and temporal domains. It is a notoriously onerous and challenging task to construct the fingerprint database for accurate localization, as the BLE RSSs must be captured via a full space scan from one point to another every few meters in a certain period of time. In order to tackle this problem, this study proposes an approach to fast fingerprints construction that only requires a sparse sampling of RSS of the space. First, a smartphone records the time series of RSS over a designated path, and a radio map for the path is then generated by a spatio-temporal mapping method using the Pedestrian Dead Reckoning algorithm. Second, the radio map of the entire space can be obtained via Gauss Process Regression (GPR), with outliers reduced to improve the reliability of the fingerprint database. Experiments have been performed in an underground carpark (38 m $$\times $$ × 14 m), and the experimental results indicate that the proposed approach can construct the fingerprint database 300% faster than the conventional approach does. The localization accuracy of both approaches is quite similar (80% error in 2.8 m). The proposed approach offers potential for the construction of a large-scale fingerprint database for a wide-area Location Based Service (LBS) of Smart City indoor and outdoor integration, where big RSS data processing is a must.

中文翻译：

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室内定位中通过具有稀疏RSS采样的高时空分辨率GPR快速构建指纹

有效的室内定位在很大程度上依赖于与射频源相关的接收信号强度 (RSS) 指纹数据库（模型），例如最广泛使用的蓝牙低功耗 (BLE) iBeacon。RSS 在空间和时间域中都表现出显着的随机变化。构建指纹数据库以进行准确定位是一项众所周知的繁重和具有挑战性的任务，因为必须在一定时间内每隔几米通过全空间扫描从一个点到另一个点捕获 BLE RSS。为了解决这个问题，本研究提出了一种快速构建指纹的方法，只需要对空间的 RSS 进行稀疏采样。首先，智能手机通过指定路径记录RSS的时间序列，然后使用行人航位推算算法通过时空映射方法生成路径的无线电地图。其次，可以通过高斯过程回归（GPR）获得整个空间的射电图，减少异常值以提高指纹数据库的可靠性。已经在地下停车场（38 m $$\times $$ × 14 m）进行了实验，实验结果表明，所提出的方法可以比传统方法更快地构建指纹数据库300%。两种方法的定位精度非常相似（2.8 m 中的误差为 80%）。所提出的方法为构建大规模指纹数据库提供了潜力，用于智能城市室内外集成的广域定位服务（LBS），其中必须进行大 RSS 数据处理。