Indoor finerprint localization algorithm based on deep belif network

doi:10.3969/j.issn.1000-1565.2021.03.015

Abstract

Abstract: WLAN-based fingerprint recognition technology measures Received Signal Strength(RSS)from different APs at a given location of each fingerprint to construct the fingerprint. However, due to changes in the environment, fingerprint collection needs to be updated regularly to improve accuracy. Therefore, in order to reduce the workload of fingerprint identification, the deep belief network algorithm is applied to the unlabelled RSS measurements, to generate probability model to represent the level of the different hidden layer implied characteristics, to extract the hidden features of fingerprint through preliminary training and tuning phase, and to keep the positioning accuracy under the premise of reducing tag fingerprints as much as possible. Experimental results show that this algorithm improves the positioning accuracy by 1.876m when only 15% of the fingerprint is used.

Key words: fingerprint localization, deep belief network, RSS, feature extraction, unsupervised learning

CLC Number:

TP391

QIN Yiwen, FENG Zhihong, SU Nan, MA Ding, LI Xiaofei. Indoor finerprint localization algorithm based on deep belif network[J]. Journal of Hebei University(Natural Science Edition), 2021, 41(3): 321-328.

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