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Improving Indoor Positioning With Adaptive Noise Modeling
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Sony Europe B.V., Lund, Sweden.ORCID-id: 0000-0003-1858-9645
2020 (Engelska)Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 8, s. 227213-227221Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

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.

Ort, förlag, år, upplaga, sidor
IEEE, 2020. Vol. 8, s. 227213-227221
Nyckelord [en]
Kalman filters, Adaptation models, Noise measurement, Bluetooth, Stochastic processes, Receivers, Process control, Adaptive noise, BLE, indoor location, indoor positioning, unscented kalman filter
Nationell ämneskategori
Signalbehandling
Identifikatorer
URN: urn:nbn:se:mau:diva-40111DOI: 10.1109/ACCESS.2020.3045615ISI: 000604515600001Scopus ID: 2-s2.0-85108304308OAI: oai:DiVA.org:mau-40111DiVA, id: diva2:1523336
Tillgänglig från: 2021-01-28 Skapad: 2021-01-28 Senast uppdaterad: 2023-10-17Bibliografiskt granskad
Ingår i avhandling
1. Scaling Indoor Positioning: improving accuracy and privacy of indoor positioning
Öppna denna publikation i ny flik eller fönster >>Scaling Indoor Positioning: improving accuracy and privacy of indoor positioning
2023 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Our phones have many uses for positioning technologies, such as navigation, LocationBased Services (LBS), emergency positioning, fitness applications, and advertising. We trust our phones and wearables to be location-aware. However, as soon as we enter a building, we can no longer use GPS signals, as their already weak signals are well below the background noise of the environment. This requires us to develop alternatives, such as installing active radio beacons, using existing radio infrastructure, applying environmental sensing based on barometric pressure and magnetic fields, or utilizing Inertial Measurement Units (IMUs) to estimate the user location. This licentiate thesis aims to evaluate beacon-based indoor positioning, where we assume installing a set of small battery-powered Bluetooth low-energy (BLE) beacons are possible. In particular, the thesis addresses essential factors such as installation effort, accuracy, the privacy aspects of an Indoor Positioning System(IPS), and mitigation of accuracy issues related to radio signal shadowing in complex indoor environments. The goal is to solve some obstacles to the widespread adoption of indoor positioning solutions.

Ort, förlag, år, upplaga, sidor
Malmö: Malmö University Press, 2023. s. 61
Serie
Studies in Computer Science ; 24
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-62916 (URN)10.24834/isbn.9789178774234 (DOI)978-91-7877-422-7 (ISBN)978-91-7877-423-4 (ISBN)
Presentation
2023-11-07, F415, Orkanen, Nordenskiöldsg. 10, 15:00
Opponent
Handledare
Tillgänglig från: 2023-10-03 Skapad: 2023-10-03 Senast uppdaterad: 2023-11-17Bibliografiskt granskad

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