A Scalable Lightweight Radio Fingerprint Map Construction Method

LIU Wenyuan; LIU Huixiang; WEN Liyun; WANG Lin

doi:10.11999/JEIT170338

Volume 40 Issue 2

Feb. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(2): 306-313

LIU Wenyuan, LIU Huixiang, WEN Liyun, WANG Lin. A Scalable Lightweight Radio Fingerprint Map Construction Method[J]. Journal of Electronics & Information Technology, 2018, 40(2): 306-313. doi: 10.11999/JEIT170338

Citation:

LIU Wenyuan, LIU Huixiang, WEN Liyun, WANG Lin. A Scalable Lightweight Radio Fingerprint Map Construction Method[J]. Journal of Electronics & Information Technology, 2018, 40(2): 306-313. doi: 10.11999/JEIT170338

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A Scalable Lightweight Radio Fingerprint Map Construction Method

doi: 10.11999/JEIT170338 cstr: 32379.14.JEIT170338

1.
2.
(School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004,China)
3.
(Information Center in Quality and Technical Supervision Bureau of Hebei Province, Shijiazhuang 050091, China)

Funds:

The National Natural Science Foundation of China (61672448, 61772453), The Technology Foundation for Selected Overseas Chinese of Hebei Province (CL201625)

Received Date: 2017-04-17
Rev Recd Date: 2017-09-11
Publish Date: 2018-02-19

Abstract

Abstract

Fingerprint-based indoor localization technology is attracted extensive attention of researchers with the fusion of crowd-sensing and machine learning. However, existing approaches have the bottleneck of scalability and instantaneity caused by high radio map construction effort. Focusing on this issue, this paper proposes a novel and scalable lightweight radio map construction method, named FFIL. In the fingerprint construction phase, the whole indoor environment is divided into multi-loop to segment map rapidly and fingerprint data are obtained. In the fingerprint matching phase, the distance is calculated from Access Point (AP) to target firstly, and then the reference point is selected on the loop with most similar with the circle radius to match fingerprint data one by one. In the localization phase, contour-based clustering algorithm is used to improve the positioning accuracy. Abundant simulations and experiments are driven by real data show that FFIL can reduce the overhead of constructing radio fingerprint map and improve the positioning accuracy and the real-time performance of system simultaneously.
- Indoor localization,
- Fingerprint,
- WiFi,
- Contour,
- Positioning accuracy

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References(22)

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