التفاصيل البيبلوغرافية
| العنوان: |
An Adaptive Human Activity-Aided Hand-Held Smartphone-Based Pedestrian Dead Reckoning Positioning System. |
| المؤلفون: |
Wu, Bang1 (AUTHOR) bang.wu@qmul.ac.uk, Ma, Chengqi2 (AUTHOR) chengqi.ma.16@ucl.ac.uk, Poslad, Stefan1 (AUTHOR) stefan.poslad@qmul.ac.uk, Selviah, David R.2 (AUTHOR) |
| المصدر: |
Remote Sensing. Jun2021, Vol. 13 Issue 11, p2137. 1p. |
| مصطلحات موضوعية: |
*HUMAN activity recognition, *INDOOR positioning systems, *TRAJECTORY optimization, *PEDESTRIANS, *BUILDING layout |
| مستخلص: |
Pedestrian dead reckoning (PDR), enabled by smartphones' embedded inertial sensors, is widely applied as a type of indoor positioning system (IPS). However, traditional PDR faces two challenges to improve its accuracy: lack of robustness for different PDR-related human activities and positioning error accumulation over elapsed time. To cope with these issues, we propose a novel adaptive human activity-aided PDR (HAA-PDR) IPS that consists of two main parts, human activity recognition (HAR) and PDR optimization. (1) For HAR, eight different locomotion-related activities are divided into two classes: steady-heading activities (ascending/descending stairs, stationary, normal walking, stationary stepping, and lateral walking) and non-steady-heading activities (door opening and turning). A hierarchical combination of a support vector machine (SVM) and decision tree (DT) is used to recognize steady-heading activities. An autoencoder-based deep neural network (DNN) and a heading range-based method to recognize door opening and turning, respectively. The overall HAR accuracy is over 98.44%. (2) For optimization methods, a process automatically sets the parameters of the PDR differently for different activities to enhance step counting and step length estimation. Furthermore, a method of trajectory optimization mitigates PDR error accumulation utilizing the non-steady-heading activities. We divided the trajectory into small segments and reconstructed it after targeted optimization of each segment. Our method does not use any a priori knowledge of the building layout, plan, or map. Finally, the mean positioning error of our HAA-PDR in a multilevel building is 1.79 m, which is a significant improvement in accuracy compared with a baseline state-of-the-art PDR system. [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
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