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. 2016 Nov 30;16(12):2030.
doi: 10.3390/s16122030.

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

Yi Lu  1   2 , Dongyan Wei  3 , Qifeng Lai  4   5 , Wen Li  6   7 , Hong Yuan  8
Affiliations

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

Yi Lu et al. Sensors (Basel). .

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.

Keywords: HMM; PDR; context recognition; indoor localization; turn detection.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The diagram of the proposed method.
Figure 2
Figure 2
The heading determination graph.
Figure 3
Figure 3
The map of the parking garage at the Beijing New Technology Base of the Chinese Academy of Sciences.
Figure 4
Figure 4
Original angular velocity and the turn symbol based on original data.
Figure 5
Figure 5
Smartphone’s coordinate system and positioning.
Figure 6
Figure 6
The flow chart of the turn detection algorithm.
Figure 7
Figure 7
PDF of three movements.
Figure 8
Figure 8
Diagram of HMM.
Figure 9
Figure 9
The map of the 8th floor in the main building of the Academy of Opto-Electronics.
Figure 10
Figure 10
The graph of the Recursive Viterbi algorithm.
Figure 11
Figure 11
The matching results of different thresholds with the limitation of two contexts.
Figure 12
Figure 12
The average number of needed contexts with different thresholds.
Figure 13
Figure 13
The trajectories of different algorithm. (a) In parking garage; (b) on the 8th floor.
Figure 14
Figure 14
The comparison of positioning errors by different methods. (a) In parking garage; (b) on the 8th floor.
Figure 14
Figure 14
The comparison of positioning errors by different methods. (a) In parking garage; (b) on the 8th floor.
Figure 15
Figure 15
The comparison of positioning errors with different false rate.
See this image and copyright information in PMC

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