| Author Name | Affiliation | | Ying Xia | School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China
School of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161006, Heilongjiang, China | | Zhongzhao Zhang | School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China | | Lin Ma | School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China | | Yao Wang | School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China |
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| Abstract: |
| With the rapid development of WLAN (Wireless Local Area Network) technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online computation. In this paper, it proposes a novel fingerprint positioning algorithm known as semi-supervised affinity propagation clustering based on distance function constraints. We show that by employing affinity propagation techniques, it is able to use a fractional labeled data to adjust similarity matrix of signal space to cluster reference points with high accuracy. The semi-supervised APC uses a combination of machine learning, clustering analysis and fingerprinting algorithm. By collecting data and testing our algorithm in a realistic indoor WLAN environment, the experimental results indicate that the proposed algorithm can improve positioning accuracy while reduce the online localization computation, as compared with the widely used K nearest neighbor and maximum likelihood estimation algorithms. |
| Key words: wireless local area network (WLAN) semi-supervised similarity matrix clustering affinity propagation |
| DOI：10.11916/j.issn.1005-9113.2015.06.008 |
| Clc Number:TP393.17 |
| Fund: |
