Questions /Wireless sensing and localization
What datasets and tools exist for wireless localization?
Start with a combination of real measurement data, localization-specific code, and at least one system that exposes the hardware assumptions.
Short answer
Good localization work needs more than a cleaned benchmark file. You need to understand the sensing modality, the anchor assumptions, and the calibration burden that sits behind the data.
The most useful starting point is usually a paired set of artifacts: one dataset that exposes the problem structure and one codebase or toolchain that shows how the data is actually used.
What to do next
- Prefer datasets that describe hardware, environment, and labeling assumptions clearly.
- Check whether the code captures deployment details or only a model training loop.
- Use one real dataset before committing to a new collection campaign.
Research areas to open next
Representative papers
Useful tools and datasets
People and group context
When to reach out
Reach out when the existing public datasets are close but not enough, and the gap is clearly tied to your deployment or sensing modality.
Related questions
What is wireless sensing?
Wireless sensing uses communication signals themselves as measurements of people, objects, motion, geometry, or physical interaction.
Can WiFi or BLE be used for indoor localization?
Yes, but performance depends on geometry, calibration, bandwidth, anchors, and how much infrastructure control you actually have.
How can wireless systems be spoofed or tracked?
Wireless systems leak identity and state through signals, timing, metadata, and physical-layer behavior that are often ignored during system design.