Uncertainty-based Fingerprinting Model Selection for Radio Localization

Stahlke M, Feigl T, Kram S, Eskofier B, Mutschler C (2023)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2023

Pages Range: 1-6

Conference Proceedings Title: Proc. 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2023)

Event location: Nuremberg, Germany DE

DOI: 10.1109/IPIN57070.2023.10332531

Abstract

Indoor radio environments often consist of areas with mixed propagation conditions. In LoS-dominated areas, classic ToF methods reliably return optimal (accurate) positions, while in NLoS-dominated areas (AI-based) fingerprinting methods are required. However, these fingerprinting methods are only cost-efficient if they are used exclusively in NLoS-dominated areas due to an expensive life cycle management. Systems that are both accurate and cost-efficient in LoS- and NLoS-dominated areas require an identification of those areas to select the optimal localization method. In this paper we propose methods for uncertainty estimation of AI-based fingerprinting to determine its validity. Our experiments show that we can implicitly switch between classic and fingerprinting-based approaches to reliably estimate accurate positions, even in NLoS-dominated radio environments. Our approach works even if the AI models are only trained on radio data in certain areas of the environment. In contrast to the state-of-the-art, our approach intrinsically identifies the spatial boundaries of the AI model, and thus does not require prior area identification.

Authors with CRIS profile

Maximilian Stahlke Machine Learning and Data Analytics Lab Tobias Feigl Lehrstuhl für Informatik 2 (Programmiersysteme) Björn Eskofier Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Christopher Mutschler Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics)

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Stahlke, M., Feigl, T., Kram, S., Eskofier, B., & Mutschler, C. (2023). Uncertainty-based Fingerprinting Model Selection for Radio Localization. In Proc. 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2023) (pp. 1-6). Nuremberg, Germany, DE.

MLA:

Stahlke, Maximilian, et al. "Uncertainty-based Fingerprinting Model Selection for Radio Localization." Proceedings of the 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Nuremberg, Germany 2023. 1-6.

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