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Sensors 2016, 16(12), 2030; doi:10.3390/s16122030

A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model

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1,* , 1,2
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1,2
and
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1
Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Academic Editors: Lyudmila Mihaylova, Byung-Gyu Kim and Debi Prosad Dogra
Received: 17 August 2016 / Revised: 18 November 2016 / Accepted: 22 November 2016 / Published: 30 November 2016
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
View Full-Text   |   Download PDF [7325 KB, uploaded 30 November 2016]   |  

Abstract

Indoor positioning has recently become an important field of interest because global navigation satellite systems (GNSS) are usually unavailable in indoor environments. Pedestrian dead reckoning (PDR) is a promising localization technique for indoor environments since it can be implemented on widely used smartphones equipped with low cost inertial sensors. However, the PDR localization severely suffers from the accumulation of positioning errors, and other external calibration sources should be used. In this paper, a context-recognition-aided PDR localization model is proposed to calibrate PDR. The context is detected by employing particular human actions or characteristic objects and it is matched to the context pre-stored offline in the database to get the pedestrian’s location. The Hidden Markov Model (HMM) and Recursive Viterbi Algorithm are used to do the matching, which reduces the time complexity and saves the storage. In addition, the authors design the turn detection algorithm and take the context of corner as an example to illustrate and verify the proposed model. The experimental results show that the proposed localization method can fix the pedestrian’s starting point quickly and improves the positioning accuracy of PDR by 40.56% at most with perfect stability and robustness at the same time. View Full-Text
Keywords: PDR; context recognition; HMM; indoor localization; turn detection PDR; context recognition; HMM; indoor localization; turn detection
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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

Lu, Y.; Wei, D.; Lai, Q.; Li, W.; Yuan, H. A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model. Sensors 2016, 16, 2030.

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