Indoor localization is the problem of determining the position or location of a person or device within an indoor environment using Wi-Fi channel state information (CSI) or received signal strength, where GPS and other satellite-based methods are unreliable or unavailable. It matters to the field because it serves as one of the most widely studied benchmark tasks in Wi-Fi sensing research, directly motivating work on generalizability and reproducibility since localization models trained in one environment frequently fail when deployed in another due to changes in layout, furniture, or antenna placement. Key variants include fingerprinting-based approaches, which map CSI measurements to predefined reference points, ranging-based approaches that estimate distance from signal propagation characteristics, and coarser-grained room-level or zone-level localization as opposed to fine-grained coordinate estimation.
Source Papers
- A Survey on Fusion-Based Indoor Positioning ↗ — A Survey on Fusion-Based Indoor Positioning
- A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospects ↗ — A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techni
- A Tutorial on Privacy, RCM and Its Implications in WLAN ↗ — A Tutorial on Privacy, RCM and Its Implications in WLAN
- A survey on CSI-based Wi-Fi sensing datasets and models with a focus on reproducibility ↗ — A survey on CSI-based Wi-Fi sensing datasets and models with
- An Overview on IEEE 802.11bf: WLAN Sensing ↗ — An Overview on IEEE 802.11bf: WLAN Sensing
- 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
- Estimating indoor crowd density and movement behavior using WiFi sensing ↗ — Estimating indoor crowd density and movement behavior using
- Free Your CSI ↗ — Free Your CSI
- NeRF2: Neural Radio-Frequency Radiance Fields ↗ — NeRF2: Neural Radio-Frequency Radiance Fields
- OPERAnet, a multimodal activity recognition dataset acquired from radio frequency and vision-based sensors ↗ — OPERAnet, a multimodal activity recognition dataset acquired
- 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
- WiFi CSI-based device-free sensing: from Fresnel zone model to CSI-ratio model ↗ — WiFi CSI-based device-free sensing: from Fresnel zone model
- WiFi Sensing with Channel State Information ↗ — WiFi Sensing with Channel State Information
- WiSegRT: Dataset for Site-Specific Indoor Radio Propagation Modeling with 3D Segmentation and Differentiable Ray-Tracing: (Invited Paper) ↗ — WiSegRT: Dataset for Site-Specific Indoor Radio Propagation