| Autor: |
Lin Qi, Yu Liu, Chuanshun Gao, Tao Feng, Yue Yu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Defence Technology, Vol 33, Iss , Pp 327-338 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2214-9147 |
| DOI: |
10.1016/j.dt.2023.11.006 |
| Popis: |
Pedestrian positioning system (PPS) using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare, emergency rescue, soldier positioning, etc. The performance of traditional PPS is limited by the cumulative error of inertial sensors, complex motion modes of pedestrians, and the low robustness of the multi-sensor collaboration structure. This paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging (H-PPS). A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node, and enhanced by a novel ellipsoid constraint model. In addition, a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes, which effectively reduces the cumulative error originating from inertial sensors. Finally, a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator, and multi-level constraints are extracted to further improve the performance of the overall system. Experimental results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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