Improving Indoor Positioning With Adaptive Noise Modeling

Indoor positioning is important for applications within Internet of Things, such as equipment tracking and indoor maps. Inexpensive Bluetooth-beacons have become common for such applications, where the distance is estimated using the Received Signal Strength. Large installations require substantial...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.227213-227221
Main Author: Engstrom, Jimmy
Format: Article
Language:English
Subjects:
Adaptation models
Adaptive filters
Adaptive noise
Beacons
BLE
Bluetooth
Fingerprinting
indoor location
indoor positioning
Inertial platforms
Internet of Things
Kalman filters
Modelling
Noise
Noise measurement
Process control
Receivers
Signal strength
Stochastic processes
Tracking devices
unscented kalman filter
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Summary:Indoor positioning is important for applications within Internet of Things, such as equipment tracking and indoor maps. Inexpensive Bluetooth-beacons have become common for such applications, where the distance is estimated using the Received Signal Strength. Large installations require substantial efforts, either in determining the exact location of all beacons to facilitate lateration, or collecting signal strength data from a grid over all locations to facilitate fingerprinting. To reduce this initial setup cost, one may infer the positions using Simultaneous Location and Mapping. In this paper, we use a mobile phone equipped with an Inertial Measurement Unit, a Bluetooth receiver, and an Unscented Kalman Filter to infer beacon positions. Further, we apply adaptive noise modeling in the filter based on the estimated distance of the beacons, in contrast to using a fixed noise estimate which is the common approach. This gives us more granular control of how much impact each signal strength reading has on the position estimates. The adaptive model decreases the beacon positioning errors by 27% and the user positioning errors by 21%. The positioning accuracy is 0.3 m better compared to using known beacon positions with fixed noise, while the effort to setup and maintain the position of each beacon is also substantially reduced. Therefore, adaptive noise modeling of Received Signal Strength is a significant improvement over static noise modeling for indoor positioning.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3045615