Semi-supervised Indoor Fingerprint Database Construction Method Based on the Nonhomogeneous Distribution Characteristic of Received Signal Strength

Shibao LI; Shengzhi WANG; Jianhang LIU; Tingpei HUANG; Xin ZHANG

doi:10.11999/JEIT180599

Volume 41 Issue 10

Oct. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(10): 2302-2309

Shibao LI, Shengzhi WANG, Jianhang LIU, Tingpei HUANG, Xin ZHANG. Semi-supervised Indoor Fingerprint Database Construction Method Based on the Nonhomogeneous Distribution Characteristic of Received Signal Strength[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2302-2309. doi: 10.11999/JEIT180599

Citation:

Shibao LI, Shengzhi WANG, Jianhang LIU, Tingpei HUANG, Xin ZHANG. Semi-supervised Indoor Fingerprint Database Construction Method Based on the Nonhomogeneous Distribution Characteristic of Received Signal Strength[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2302-2309. doi: 10.11999/JEIT180599

Citation:

Shibao LI, Shengzhi WANG, Jianhang LIU, Tingpei HUANG, Xin ZHANG. Semi-supervised Indoor Fingerprint Database Construction Method Based on the Nonhomogeneous Distribution Characteristic of Received Signal Strength[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2302-2309. doi: 10.11999/JEIT180599

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Semi-supervised Indoor Fingerprint Database Construction Method Based on the Nonhomogeneous Distribution Characteristic of Received Signal Strength

doi: 10.11999/JEIT180599

College of Computer and Communication Engineering, China University of Petroleum (East China), Qingdao 266580, China

Funds: The National Natural Science Foundation of China (61972417, 61601519, 61872385), The Fundamental Research Funds for the Central Universities (18CX02134A, 18CX02137A, 18CX02133A, 19CX05003A-4)

Received Date: 2018-06-20
Rev Recd Date: 2019-02-28

Available Online: 2019-03-30

Publish Date: 2019-10-01

Abstract

Abstract

The radio map construction is time consuming and labor intensive, and the conventional semi-supervised based methods usually ignore the influence of the uneven distribution of high-dimensional Received Signal Strength (RSS). In order to solve that problem, a semi-supervised radio map construction approach which is based on the nonhomogeneous distribution characteristic of RSS is proposed. The approach utilizes the RSS local scale and common neighbors similarities to calculate the weighted matrix. Thus, the weighted graph that reflects accurately the structure of RSS data manifold is presented. In addition, the weighted graph is used to find the optimal solution of the objective function to calibrate the locations of plenty of unlabeled data by a small number of labeled RSS. The extensive experiments demonstrate that the proposed method is capable of not only constructing an accurate radio map at a low manual cost, but also achieving a high localization accuracy.

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References(19)

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