Journal
EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING
Volume -, Issue -, Pages -Publisher
SPRINGEROPEN
DOI: 10.1186/1687-1499-2013-272
Keywords
WLAN indoor positioning; Fingerprinting; Affinity propagation clustering; RSS; Probability distribution
Funding
- National Science and Technology Major Project of China [2012ZX03006-002(3)]
- National Natural Science Foundation of China [61301126]
- Fundamental and Frontier Research Project of Chongqing [cstc2013jcyjA40032, cstc2013jcyjA40034, cstc2013jcyjA40041]
- Special Fund of Chongqing Key Laboratory (CSTC)
- Science and Technology Project of Chongqing Municipal Education Commission [KJ130528]
- Startup Foundation for Doctors of CQUPT [A2012-33]
- Science Foundation for Young Scientists of CQUPT [A2012-77]
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Recently, the fingerprint-based wireless local area network (WLAN) positioning has gained significant interest. A probability distribution-aided indoor positioning algorithm based on the affinity propagation clustering is proposed. Different from the conventional fingerprint-based WLAN positioning algorithms, the paper first utilizes the affinity propagation clustering to minimize the searching space of reference points (RPs). Then, we introduce the probability distribution-aided positioning algorithm to obtain the target's refined position. Furthermore, because the affinity clustering can effectively lead to a reduction of the computational cost for the RP searching which is involved in the probability distribution-aided positioning algorithm, the proposed algorithm can lower the difficulty and minimize the power consumption when estimating the user's position. Experimental results conducted in the real environments show that our proposed algorithm will significantly improve the performance of the probability distribution-aided positioning algorithm in both the positioning accuracy and real-time ability.
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