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Sensors 2016, 16(9), 1447; doi:10.3390/s16091447

An Indoor Pedestrian Positioning Method Using HMM with a Fuzzy Pattern Recognition Algorithm in a WLAN Fingerprint System

1
Computer and Network Center, Communication University of China, No. 1 Dingfuzhuang East Street, Chaoyang District, Beijing 100024, China
2
Information Engineering Institute, Science and Technology Department, Communication University of China, No. 1 Dingfuzhuang East Street, Chaoyang District, Beijing 100024, China
3
School of Journalism, Faculty of Journalism and Communication, Communication University of China, No. 1 Dingfuzhuang East Street, Chaoyang District, Beijing 100024, China
*
Author to whom correspondence should be addressed.
Academic Editor: Fan Ye
Received: 4 July 2016 / Revised: 25 August 2016 / Accepted: 5 September 2016 / Published: 8 September 2016
(This article belongs to the Section Physical Sensors)
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Abstract

With the rapid development of smartphones and wireless networks, indoor location-based services have become more and more prevalent. Due to the sophisticated propagation of radio signals, the Received Signal Strength Indicator (RSSI) shows a significant variation during pedestrian walking, which introduces critical errors in deterministic indoor positioning. To solve this problem, we present a novel method to improve the indoor pedestrian positioning accuracy by embedding a fuzzy pattern recognition algorithm into a Hidden Markov Model. The fuzzy pattern recognition algorithm follows the rule that the RSSI fading has a positive correlation to the distance between the measuring point and the AP location even during a dynamic positioning measurement. Through this algorithm, we use the RSSI variation trend to replace the specific RSSI value to achieve a fuzzy positioning. The transition probability of the Hidden Markov Model is trained by the fuzzy pattern recognition algorithm with pedestrian trajectories. Using the Viterbi algorithm with the trained model, we can obtain a set of hidden location states. In our experiments, we demonstrate that, compared with the deterministic pattern matching algorithm, our method can greatly improve the positioning accuracy and shows robust environmental adaptability. View Full-Text
Keywords: pedestrian positioning; fuzzy pattern recognition algorithm; RSSI variation trend; hidden markov model; smartphone; fingerprint system pedestrian positioning; fuzzy pattern recognition algorithm; RSSI variation trend; hidden markov model; smartphone; fingerprint system
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ni, Y.; Liu, J.; Liu, S.; Bai, Y. An Indoor Pedestrian Positioning Method Using HMM with a Fuzzy Pattern Recognition Algorithm in a WLAN Fingerprint System. Sensors 2016, 16, 1447.

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