title: A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model.
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.
authors.affiliation: Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China. luyi_aoe@163.com.|University of Chinese Academy of Sciences, Beijing 100049, China. luyi_aoe@163.com.
authors.forename: Yi
authors.initials: Y
authors.lastname: Lu
authors.affiliation: Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China. weidongyan@aoe.ac.cn.
authors.forename: Dongyan
authors.initials: D
authors.lastname: Wei
authors.affiliation: Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China. laiqifeng@aoe.ac.cn.|University of Chinese Academy of Sciences, Beijing 100049, China. laiqifeng@aoe.ac.cn.
authors.forename: Qifeng
authors.initials: Q
authors.lastname: Lai
authors.affiliation: Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China. wen.li@aoe.ac.cn.|University of Chinese Academy of Sciences, Beijing 100049, China. wen.li@aoe.ac.cn.
authors.forename: Wen
authors.initials: W
authors.lastname: Li
authors.affiliation: Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China. yuanh@aoe.ac.cn.
authors.forename: Hong
authors.initials: H
authors.lastname: Yuan
keywords: HMM; PDR; context recognition; indoor localization; turn detection