Leveraging Machine Learning for Enhanced Indoor Positioning Accuracy and Reliability
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".
Deadline for manuscript submissions: 15 June 2025 | Viewed by 139
Special Issue Editors
Interests: passive radar; activity recognition; remote sensing; ISAC; digital health
Special Issues, Collections and Topics in MDPI journals
Interests: integrated sensing; computation and communication; wireless sensor networks; mobile edge computing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Indoor localization has gained significant importance for various applications, including navigation, asset tracking, and location-based services in complex environments such as shopping malls, airports, hospitals, and industrial settings. Unlike GPS signals, indoor localization faces challenges like signal interference, multipath propagation, and environmental complexities that can degrade accuracy. Machine learning (ML) techniques, with their ability to model complex patterns and adapt to changing environments, offer promising solutions to enhance the performance of indoor positioning systems. This Special Issue aims to gather the latest advances in research and developments at the intersection of machine learning and indoor localization. Topics of interest for this Special Issue include, but are not limited to, the following:
- Machine learning for indoor positioning;
- Explainable AI (XAI) in indoor positioning systems;
- Real-time applications;
- Prototype development;
- Sensor fusion;
- Device-based/passive systems;
- Collaborative positioning with ML;
- Energy-efficient ML algorithms for indoor positioning;
- Hybrid models combining ML with traditional methods;
- Simulation and synthetic data generation;
- Case studies and real-world implementations.
Dr. Wenda Li
Dr. Yue Tian
Dr. Shelly Vishwakarma
Guest Editors
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Keywords
- indoor positioning
- machine learning
- sensor fusion
- explainable AI (XAI)
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