Indoor positioning refers to the process of determining the location of a person or object within an enclosed environment, such as a building or multi-story facility, where global navigation satellite systems (GNSS) are unavailable or unreliable. It matters because accurate indoor localization underpins a wide range of applications including emergency response, asset tracking, smart building management, and context-aware services, driving sustained research into methods that can match the accuracy and ubiquity of outdoor GPS. Key variants include fingerprinting-based approaches that map signal signatures to known locations, geometric methods such as trilateration and triangulation using ranging or angle measurements, pedestrian dead reckoning using inertial sensors, and increasingly, fusion-based systems that combine heterogeneous sources such as WiFi, Bluetooth, ultrawideband, and IMU data within probabilistic or learning-based frameworks to improve robustness and accuracy across diverse indoor spatial contexts.
Source Papers
- A Standard Indoor Spatial Data Model—OGC IndoorGML and Implementation Approaches ↗ — A Standard Indoor Spatial Data Model—OGC IndoorGML and Imple
- A Survey on Fusion-Based Indoor Positioning ↗ — A Survey on Fusion-Based Indoor Positioning
- Channel State Information from Pure Communication to Sense and Track Human Motion: A Survey ↗ — Channel State Information from Pure Communication to Sense a
- Deep Learning-Enhanced Human Sensing with Channel State Information: A Survey ↗ — Deep Learning-Enhanced Human Sensing with Channel State Info
- Time matters: Empirical insights into the limits and challenges of temporal generalization in CSI-based Wi-Fi sensing ↗ — Time matters: Empirical insights into the limits and challen