Vector Graph Assisted Pedestrian Dead Reckoning Using an Unconstrained Smartphone
Abstract
:1. Introduction
2. Motivation and Paper Outline
3. Indoor Localization System Architecture
4. Pedestrian Dead Reckoning
4.1. Step Detection
- C1.
- The total acceleration magnitude amag has to cross the threshold δth from negative to positive.
- C2.
- The time interval ∆t between two consecutive steps defined by C1 must be within the interval threshold from ∆tmin to ∆tmax.
- C3.
- The difference adif between extreme values of amag during a step phase and the threshold δth has to be among λmin to λmax, otherwise a perturbation point is recorded.
- C4.
- Transition from S2 to S1: there is no candidate step or there exist perturbation points in the sliding window.
- C5.
- Transition from S1 to S2: the candidate step number is more than one and there is not any perturbation point within the sliding window, meanwhile the autocorrelation value rmag is larger than threshold rth.
4.2. Step Length Estimation
- •
- , i.e., step length is zero when a pedestrian is static;
- •
- , i.e., step length at turning points is usually smaller than a pedestrian is walking straightly;
- •
- , i.e., step length is a constant equal to the height of sidesteps when a pedestrian is climbing stairs;
- •
- , i.e., the model parameters βSi,Cj are inversely proportional to the acceleration variances in different carrying contexts. For example, the acceleration variance is the largest in In-pocket context C4 usually, thus the model parameter βSi,C4 is smaller than the parameters in other contexts.
Carrying Contexts | Recognized Carrying Contexts | |||
---|---|---|---|---|
Texting | Calling | In-Hand | In-Pocket | |
Texting | 100 | 0 | 0 | 0 |
Calling | 0 | 98.99 | 0 | 1.01 |
In-hand | 0 | 0 | 100 | 0 |
In-pocket | 0 | 0 | 0 | 100 |
4.3. Heading Determination
5. Particle Filter Algorithm
5.1. Particle Filter Implementation
- •
- If the particles hit an obstacle in non-turning situation (heading difference is less than 25°), the step length model parameters of them are retained and can be inherited by the next generation particles;
- •
- If the particles die in turning situation (heading difference is more than 25°), their model parameters are abandoned and only the model parameters of surviving particles are retained for generating new particles.
5.2. Map Construction and Optimization
6. Indoor Localization Field Test
6.1. Field Test Setup
6.2. Field Test Results
Carrying Modes | Real-Time Localization Errors | |||
---|---|---|---|---|
50% Error | 95% Error | |||
Proposed | w/o Assistance | Proposed | w/o Assistance | |
Texting | 0.51 | 0.93 | 0.80 | 2.24 |
Calling | 0.45 | 1.14 | 0.88 | 4.54 |
In-hand | 0.50 | 1.38 | 1.01 | 3.65 |
In-pocket | 0.74 | 1.40 | 1.71 | 3.74 |
7. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Qian, J.; Pei, L.; Ma, J.; Ying, R.; Liu, P. Vector Graph Assisted Pedestrian Dead Reckoning Using an Unconstrained Smartphone. Sensors 2015, 15, 5032-5057. https://doi.org/10.3390/s150305032
Qian J, Pei L, Ma J, Ying R, Liu P. Vector Graph Assisted Pedestrian Dead Reckoning Using an Unconstrained Smartphone. Sensors. 2015; 15(3):5032-5057. https://doi.org/10.3390/s150305032
Chicago/Turabian StyleQian, Jiuchao, Ling Pei, Jiabin Ma, Rendong Ying, and Peilin Liu. 2015. "Vector Graph Assisted Pedestrian Dead Reckoning Using an Unconstrained Smartphone" Sensors 15, no. 3: 5032-5057. https://doi.org/10.3390/s150305032
APA StyleQian, J., Pei, L., Ma, J., Ying, R., & Liu, P. (2015). Vector Graph Assisted Pedestrian Dead Reckoning Using an Unconstrained Smartphone. Sensors, 15(3), 5032-5057. https://doi.org/10.3390/s150305032