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

• Published in: Sensors (Basel, Switzerland) Vol. 16; no. 12; p. 2030
• Main Authors: Lu, Yi, Wei, Dongyan, Lai, Qifeng, Li, Wen, Yuan, Hong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.11.2016; MDPI
• Subjects: Algorithms; context recognition; Dead reckoning; Global navigation satellite system; HMM; Indoor environments; indoor localization; Localization; Localization method; Markov chains; Mathematical models; Navigation satellites; PDR; Pedestrians; Position (location); turn detection; Viterbi algorithm detectors; HMM; indoor localization; context recognition; turn detection; PDR
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s16122030

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.