دورية أكاديمية
Adaptive Kalman filtering-based pedestrian navigation algorithm for smartphones
| العنوان: | Adaptive Kalman filtering-based pedestrian navigation algorithm for smartphones |
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| المؤلفون: | Chen Yu, Luo Haiyong, Zhao Fang, Wang Qu, Shao Wenhua |
| المصدر: | International Journal of Advanced Robotic Systems, Vol 17 (2020) |
| بيانات النشر: | SAGE Publishing, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Electronics LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | Electronics, TK7800-8360, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | Pedestrian navigation with daily smart devices has become a vital issue over the past few years and the accurate heading estimation plays an essential role in it. Compared to the pedestrian dead reckoning (PDR) based solutions, this article constructs a scalable error model based on the inertial navigation system and proposes an adaptive heading estimation algorithm with a novel method of relative static magnetic field detection. To mitigate the impact of magnetic fluctuation, the proposed algorithm applies a two-way Kalman filter process. Firstly, it achieves the historical states with the optimal smoothing algorithm. Secondly, it adjusts the noise parameters adaptively to reestimate current attitudes. Different from the pedestrian dead reckoning-based solution, the error model system in this article contains more state information, which means it is more sensitive and scalable. Moreover, several experiments were conducted, and the experimental results demonstrate that the proposed heading estimation algorithm obtains better performance than previous approaches and our system outperforms the PDR system in terms of flexibility and accuracy. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1729-8814 17298814 |
| العلاقة: | https://doaj.org/toc/1729-8814Test |
| DOI: | 10.1177/1729881420930934 |
| الوصول الحر: | https://doaj.org/article/4d3ceb82568145c0908c5f2abdfbaefcTest |
| رقم الانضمام: | edsdoj.4d3ceb82568145c0908c5f2abdfbaefc |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 17298814 |
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| DOI: | 10.1177/1729881420930934 |