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The Quick Radio Fingerprint Collection Method for a WiFi-Based Indoor Positioning System

机译：基于WiFi的室内定位系统的快速无线指纹采集方法

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摘要

In the WiFi-based indoor positioning system (IPS), by using WiFi access points (APs) on hand and without requiring the APs' positions, the scene analysis method has better positioning accuracy and attracts many researchers to devote their efforts to it. However, this method needs to perform a sampling process in order to collect the received signal strength indicators (RSSIs) of the APs from the places of interest to build the building's WiFi radio fingerprint database in advance. It also needs to resample frequently in order to keep the fingerprint database updated and accurate. Both tasks are time-consuming, therefore we propose a quick radio fingerprint collection (QRFC) algorithm for collecting the sampling information. The Android smartphone and its built-in motion sensors were used to help the collection process. QRFC makes the sampling process simpler, and the time needed for the sampling is close to the time of walking slowly through the path. We also propose a heuristic AP RSSI shaping algorithm to compensate for the signal attenuation caused by multi-path, shadowing, and mask effects. Several field tests were performed to compare the QRFC method to the traditional method. Experiments show that there is no significant difference in their accuracy, but QRFC takes much less time to complete.
机译：在基于WiFi的室内定位系统（IPS）中，通过使用现有的WiFi接入点（AP）而不需要AP的位置，场景分析方法具有更好的定位精度，吸引了许多研究人员投入精力。但是，此方法需要执行采样过程，以便从感兴趣的地方收集AP的接收信号强度指示器（RSSI），以预先构建建筑物的WiFi无线指纹数据库。它还需要经常重新采样，以保持指纹数据库的更新和准确性。两项任务都很耗时，因此我们提出了一种用于收集采样信息的快速无线指纹收集（QRFC）算法。 Android智能手机及其内置的运动传感器用于帮助收集过程。 QRFC使采样过程更简单，并且采样所需的时间接近缓慢通过路径的时间。我们还提出了一种启发式AP RSSI整形算法，以补偿由多径，阴影和掩码效应引起的信号衰减。进行了几次现场测试，以将QRFC方法与传统方法进行比较。实验表明，它们的准确性没有显着差异，但是QRFC花费的时间要少得多。

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