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Lee, Kyunghan
Mobile Systems and Networking Lab. (MSN)
Research Interests
  • Low-latency network, cloud computing, 5G network, mobile machine learning

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ACMI: FM-based Indoor Localization via Autonomous Fingerprinting

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Title
ACMI: FM-based Indoor Localization via Autonomous Fingerprinting
Other Titles
FM-based Indoor Localization via Automatic Fingerprint DB Construction a nd Matching
Author
Yoon SungroLee, KyunghanYun, YeoCheonRhee, Injong
Issue Date
2016-06
Publisher
IEEE COMPUTER SOC
Citation
IEEE TRANSACTIONS ON MOBILE COMPUTING, v.15, no.6, pp.1318 - 1332
Abstract
We present ACMI, an FM-based indoor localization system that does not require proactive site profiling. ACMI constructs the fingerprint database based on pure estimation of indoor RSS (received signal strength) distribution, where only the signals transmitted from commercial FM radio stations are used. Based on extensive field measurement study, we established our own signal propagation model that harnesses FM radio characteristics and open information of FM transmission towers in combination with the floor-plan of a building. Output of the model is an RSS fingerprint database. Using the fingerprint database as a knowledge base, ACMI refines a positioning result via the two-step process; parameter calibration and path matching, during its runtime. Without site profiling, our evaluation indicates that ACMI in 7 campus locations and 3 downtown buildings using 8 distinguished FM stations finds positions with only about 6 and 10 meters of errors on average, respectively.
URI
https://scholarworks.unist.ac.kr/handle/201301/13401
URL
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7181720
DOI
10.1109/TMC.2015.2465372
ISSN
1536-1233
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