Abstract
Location fingerprinting suffers in dynamic environments and needs recalibration from time to time to maintain system performance. This paper proposes an adaptive approach for location fingerprinting. Based on real-time received signal strength indicator (RSSI) samples measured by a group of reference devices, the approach applies a modified Universal Kriging (UK) interpolant to estimate adaptive temporal and environmental radio maps. The modified UK can take the spatial distribution characteristics of RSSI into account. In addition, the issue of device heterogeneity caused by multiple reference devices is further addressed. To compensate the measuring differences of heterogeneous reference devices, differential RSSI metric is employed. Extensive experiments were conducted in an indoor field and the results demonstrate that the proposed approach not only adapts to dynamic environments and the situation of changing APs’ positions, but it is also robust toward measuring differences of heterogeneous reference devices.
Highlights
The explosive growing of location-based services (LBS) has brought high demands for location information, and fostered research interest in positioning technology
Each curve for an access points (AP) was obtained by using a different manner, and it represents quantitative structural characteristics of Received signal strength indicator (RSSI)’ spatial distribution in the test area
We find that the obtained statistical results based on the difference of travel distances of radio signals are similar with the results based on the conventional manner
Summary
The explosive growing of location-based services (LBS) has brought high demands for location information, and fostered research interest in positioning technology. Received signal strength indicator (RSSI)-based location fingerprinting approaches are often adopted in indoor wireless positioning systems. The radio map is a combination of signal strength profiles of the APs that cover the target area. It is always approximated by discrete location fingerprints collected at predefined calibration points. Based on the constructed radio map, online RSSI measurements are matched to find the most similar location fingerprints which are further used for location estimation [1]. The radio map needs continuous updating to accommodate the changing environments. Environmental dynamics, such as people moving, and doors opening and closing can cause rapid RSSI fluctuations. All the mentioned factors can lead to an outdated radio map which deteriorates the location fingerprinting performance and repeated site surveys or calibrations are needed
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