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| DBSCAN Based Subspace Matching for Indoor Cellular Network Fingerprint Positioning Algorithm |
| TIAN Zengshan WANG Xiangyong ZHOU Mu LI Lingxia |
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Abstract For the sake of reducing the indoor localization errors caused by dynamic signal fading in cellular network, this paper propose a novel Density-Based Spatial Clustering of Applications with Noise (DBSCAN) based subspace matching algorithm for indoor localization, which can improve the localization accuracy by eliminating the location with large errors. Specifically, the signal space is firstly divided into several subspaces, where a position estimation can be obtained respectively using the Weighted K Nearest Neighbors (WKNN) approach. Then, DBSCAN is applied to the position coordinates obtained from each subspace which eliminates the outliers. Finally, the location is estimated based on probability analysis. Experimental results show that the proposed approach can improve the location accuracy by eliminating the location with large errors.
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Received: 22 July 2016
Published: 28 February 2017
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| Fund: The National Natural Science Foundation of China (61301126), The Program for Changjiang Scholars and Innovative Research Team in University (IRT1299), The Fundamental and Frontier Research Project of Chongqing (cstc2013jcyjA40041, cstc2015jcyjBX0065), The Young Science Research Program of Chongging University of Posts and Telecommunications (A2013-31) |
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Corresponding Authors:
WANG Xiangyong
E-mail: wangxiangyong1991@163.com
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