Uncertainty-based Fingerprinting Model Selection for Radio Localization

• Main Authors: Stahlke, Maximilian, Feigl, Tobias, Kram, Sebastian, Eskofier, Bjoern M., Mutschler, Christopher
• Format: Conference Proceeding
• Language: English
• Subjects: Fingerprint recognition; Fingerprinting; Hybrid localization; Indoor radio communication; Location awareness; Maintenance engineering; Rendering (computer graphics); Switches; Uncertainty; Uncertainty Quantification; UWB Localization
• ISSN: 2471-917X
• DOI: 10.1109/IPIN57070.2023.10332531

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.