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

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 int...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 16; číslo 9; s. 1447
Hlavní autoři: Ni, Yepeng, Liu, Jianbo, Liu, Shan, Bai, Yaxin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 08.09.2016
MDPI
Témata:
Accuracy
Algorithms
fingerprint system
Fuzzy
fuzzy pattern recognition algorithm
hidden markov model
Mathematical models
Pattern recognition
pedestrian positioning
Pedestrians
Position (location)
RSSI variation trend
smartphone
Wireless networks
hidden markov model
smartphone
RSSI variation trend
pedestrian positioning
fingerprint system
fuzzy pattern recognition algorithm
ISSN:1424-8220, 1424-8220
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Shrnutí: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s16091447