Adaptive Affine Propagation Clustering Algorithm for WiFi Indoor Positioning

Jiusong HU; Hongli LIU; Guoxuan XIAO; Kun XU

doi:10.11999/JEIT180186

Volume 40 Issue 12

Nov. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(12): 2889-2895

Jiusong HU, Hongli LIU, Guoxuan XIAO, Kun XU. Adaptive Affine Propagation Clustering Algorithm for WiFi Indoor Positioning[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2889-2895. doi: 10.11999/JEIT180186

Citation:

Jiusong HU, Hongli LIU, Guoxuan XIAO, Kun XU. Adaptive Affine Propagation Clustering Algorithm for WiFi Indoor Positioning[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2889-2895. doi: 10.11999/JEIT180186

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Adaptive Affine Propagation Clustering Algorithm for WiFi Indoor Positioning

doi: 10.11999/JEIT180186

Jiusong HU^{1, 2},
Hongli LIU^{1
,
,},
Guoxuan XIAO³,
Kun XU¹

1.
College of Electrical and Information Engineering, Hunan University, Changsha 410006, China
2.
College of Traffic Engineering, Hunan University of Technology, Zhuzhou 412000, China
3.
State Grid Yueqing Electric Power Supply Company, Yueqing, 325600, China

Funds: The Central State-Owned Capital Management and Budget Project (2013-470), The National Natural Science Foundation of China (61771191)

Received Date: 2018-02-10
Rev Recd Date: 2018-09-03

Available Online: 2018-09-10

Publish Date: 2018-12-01

Abstract

Abstract

There are a large number of indoor WiFi signals which can be used for indoor positioning. Although many WiFi indoor positioning technology is proposed, it's positioning accuracy still does not meet the actual application requirements. For this problem, an Adaptive Affinity Propagation Clustering (AAPC) algorithm is proposed to improve the clustering quality of WiFi fingerprint, thus improving the positioning accuracy. The AAPC algorithm generates different clustering results by dynamically adjusting parameters, then cluster validity indices are used to select the best ones. A large number of real environmental data are collected and tested. The experimental results show that the clustering results generated by AAPC algorithm have higher positioning accuracy.
- WiFi indoor positioning,
- Adaptive Affine Propagation Clustering (AAPC) algorithm,
- Cluster validity indices

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References(22)

References

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