Abstract
In smartphones several sensors and receivers are embedded which enable positioning in Location-based Services and other navigation applications. They include GNSS receivers and Wireless Fidelity (Wi-Fi) cards as well as inertial sensors, such as accelerometers, gyroscope and magnetometer. In this paper, indoor Wi-Fi positioning is studied based on trilateration. Three methods are investigated which are a resection, a calculation of the center of gravity point and a differential approach. The first approach is a commonly employed resection using the ranges to the Wi-Fi Access Points (APs) as radii and intersect the circles around the APs. In the second method, the center of gravity in a triangle of APs is calculated with weighting of the received signal strength (RSS) of the Wi-Fi signals. The third approach is developed by analogy to Differential GNSS (DGNSS) and therefore termed Differential Wi-Fi (DWi-Fi). Its advantage is that a real-time modeling of the temporal RSS variations and fluctuations is possible. For that purpose, reference stations realized by low-cost Raspberry Pi units are deployed which serve at the same time as APs. The experiments conducted in a laboratory and entrance of an office building showed that position deviations from the ground truth of around 2 m are achievable with the second and third method. Thereby the positioning accuracies depend mainly on the geometrical point location in the triangle of APs and reference stations and the RSS scan duration.
Highlights
The motivation of this study is that for indoor positioning there is still no generally valid solution
Thereby the biggest influence on the obtaining positioning results was the received signal strength (RSS) scan duration of the different smartphones depending on the device specific Wi-Fi chip
This would be the case under real world conditions, such as those found in buildings with other users moving around
Summary
The motivation of this study is that for indoor positioning there is still no generally valid solution. At the beginning of the recording, large fluctuations can be observed This can be attributed to the fact that the RPs did not start broadcasting Wi-Fi signals at the same time as they were sequentially started. These fluctuations can be omitted for the evaluation. Time-dependent correction parameters are estimated using known reference stations as it is done in DGNSS [8]. In a networked solution the correction parameters can be derived similar as in a CORS (Continuous Operating Reference Station) GNSS network. Area correction parameters referred to as with the German term Flächenkorrekturparameter (FKP) are estimated and applied by the user [6]
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