Indoor Positioning System with Pedestrian Dead Reckoning and BLE Inverse Fingerprinting

Abstract

Since the adoption of Bluetooth Low Energy (BLE) in the Bluetooth standard in 2010, BLE beacons are emerging as one of the most viable solutions for indoor localization due to its power efficient architecture, short scan duration, low cost chipset, and wide adoption in the devices. The existing indoor positioning systems based on BLE beacons employ the classical fingerprinting (FP) technique where user terminals collect signals from the beacons and do most of localization computations, requiring significant power consumption on user devices. However, constant power consumption on limited battery life of a mobile device can be problematic when it comes to supporting server-oriented tracking applications. To address this issue, we have proposed a new fingerprinting technique called inverse fingerprinting (Inv-FP), which is a server side BLE fingerprint system where most of the positioning computations are done by BLE sniffers and servers, thus minimizing the computation overhead of user devices. However, the absolute positioning schemes such as FP and Inv-FP do not use the current position estimate to determine the next position. This leads to discontiguous, irregular route prediction especially when the positioning accuracy is low, since it does not reflect the continuity of the position change according to the movement of the user. In contrast, a relative positioning scheme such as Pedestrian Dead Reckoning (PDR) determines the current position based on the previous position, reflecting the continuity of the position change but it cannot estimate the current position without the initial position. In this paper, we implement both FP and Inv-FP and evaluate their performance in small and large-scale testbeds. We analyze various characteristics of Inv-FP in comparison with the classical beacon based FP, and demonstrate that Inv-FP can match the performance of FP but with minimal power consumption on user devices. In addition, we propose a new localization algorithm that can combine Inv-FP with PDR. By integrating PDR with Inv-FP, we show that localization error can be reduced by reflecting the advantages of each method.