Development and Challenges of Indoor Positioning and Localization
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".
Deadline for manuscript submissions: 25 February 2026 | Viewed by 49
Special Issue Editors
Interests: ultrasonic indoor positioning systems; sequence design; ambient intelligence for independent living
Special Issues, Collections and Topics in MDPI journals
Interests: optical indoor positioning systems; localization algorithms; machine learning techniques; sensor fusion
Interests: indoor positioning systems; underwater positioning systems; sequence design; localization algorithms
Special Issue Information
Dear Colleagues,
Indoor positioning and localization have become essential for various applications, including smart buildings, robotics, healthcare, or logistics. Unlike outdoor positioning systems such as GPS, indoor environments present unique challenges due to multipath interference, signal attenuation, and complex spatial constraints. This Special Issue will explore the latest advancements in indoor positioning technologies, including signal-based methods (Wi-Fi, Bluetooth, UWB, RFID, acoustic, optical, etc.), vision-based approaches, sensor fusion, and machine learning techniques. Additionally, we seek to address key challenges such as accuracy, scalability, energy efficiency, pervasive and non-invasive methods and privacy concerns. Contributions may include theoretical developments, experimental results, novel algorithms, and real-world applications. We invite researchers and industry experts to submit original research papers, reviews, and case studies that advance the field of indoor positioning and localization.
Dr. María Del Carmen Pérez-Rubio
Dr. Elena Aparicio Esteve
Prof. Dr. Carlos De Marziani
Guest Editors
Manuscript Submission Information
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Keywords
- indoor positioning systems (IPSs)
- localization algorithms
- sensor fusion
- Wi-Fi, Bluetooth, UWB, and RFID-based positioning
- vision-based localization, infrared, magnetic, and ultrasound
- machine learning for indoor localization
- accuracy and error mitigation
- real-world applications
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