Abstract
With the growing development of smartphones equipped with Wi-Fi technology and the need of inexpensive indoor location systems, many researchers are focusing their efforts on the development of Wi-Fi-based indoor localization methods. However, due to the difficulties in characterizing the Wi-Fi radio signal propagation in such environments, the development of universal indoor localization mechanisms is still an open issue. In this paper, we focus on the calibration of Wi-Fi-based indoor tracking systems to be used by smartphones. The primary goal is to build an accurate and robust Wi-Fi signal propagation representation in indoor scenarios.We analyze the suitability of our approach in a smartphone-based indoor tracking system by introducing a novel in-motion calibration methodology using three different signal propagation characterizations supplemented with a particle filter. We compare the results obtained with each one of the three characterization in-motion calibration methodologies and those obtained using a static calibration approach, in a real-world scenario. Based on our experimental results, we show that the use of an in-motion calibration mechanism considerably improves the tracking accuracy.
Highlights
The research in indoor localization systems has seen substantial growth over the past decade [1,2]
We have studied the in-motion Wi-Fi signal behavior using a smartphone perspective in order to design an indoor tracking system calibration methodology to be used by such devices
In order to cope with the inherent noise of Wi-Fi signals, a Window Moving Average Filter is applied to the raw received signal strength indicator (RSSI) capture
Summary
The research in indoor localization systems has seen substantial growth over the past decade [1,2]. Due to the wide availability of Wi-Fi signals in indoor environments and the presence of Wi-Fi adapters in current smartphones, Wi-Fi based localization mechanisms have attracted most attention. The most widely used parameter is RSSI since it is an inexpensive indicator to obtain and it is available on most commercial wireless devices without need of additional hardware. This parameter is greatly affected by the indoor inherent features, which cause reflections, interference and shadowing in the signal [5]
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