Rapid deployment indoor localization without prior human participation
In this work, we propose RAD, a RApid Deployment localization framework without human sampling. The basic idea of RAD is to automatically generate a fingerprint database through space partition, of which each cell is fingerprinted by its maximum influence APs. Based on this robust location indicator...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2016
|
| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/4745 https://ink.library.smu.edu.sg/context/sis_research/article/5748/viewcontent/lcn16_short_xu.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Singapore Management University |
| Language: | English |
| Summary: | In this work, we propose RAD, a RApid Deployment localization framework without human sampling. The basic idea of RAD is to automatically generate a fingerprint database through space partition, of which each cell is fingerprinted by its maximum influence APs. Based on this robust location indicator, fine-grained localization can be achieved by a discretized particle filter utilizing sensor data fusion. We devise techniques for CIVD-based field division, graph-based particle filter, EM-based individual character learning, and build a prototype that runs on commodity devices. Extensive experiments show that RAD provides a comparable performance to the state-of-the-art RSSbased methods while relieving it of prior human participation. |
|---|