Abstract
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.
Highlights
W ITHIN context awareness, knowing the position of a device is a crucial task
When solving a Simultaneous Location and Mapping (SLAM) problem, it is common to evaluate the result based on the estimated path traveled versus ground truth, with less emphasis on the landmark accuracy
In our case, we are more interested in two other metrics: (1) the ability of a deployed system to correctly identify the landmark positions, and (2) the ability to express the amount of uncertainty the system has regarding beacon positions in an installation
Summary
W ITHIN context awareness, knowing the position of a device is a crucial task. Outdoor positioning is usually handled adequately by GPS, but positioning a device indoors is challenging due to the inability of the Global Navigation Satellite Systems (GNSS) to penetrate buildings. The environment changes when doors are open or closed, people move around, or furniture is rearranged. Ultra WideBand (UWB) [3] is a promising new technology with high accuracy using Time of Arrival (ToA), but has not yet seen widespread use due to high cost. Another positioning method is Magnetic Local Positioning Systems (MLPS), such as [4], [5], where a calibrated sensor network of magnetic fields sensors are used for achieving positioning with high accuracy
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