التفاصيل البيبلوغرافية
| العنوان: |
Improved Pedestrian Dead Reckoning Based on a Robust Adaptive Kalman Filter for Indoor Inertial Location System |
| المؤلفون: |
Qigao Fan, Hai Zhang, Peng Pan, Xiangpeng Zhuang, Jie Jia, Pengsong Zhang, Zhengqing Zhao, Gaowen Zhu, Yuanyuan Tang |
| المصدر: |
Sensors, Vol 19, Iss 2, p 294 (2019) |
| بيانات النشر: |
MDPI AG, 2019. |
| سنة النشر: |
2019 |
| المجموعة: |
LCC:Chemical technology |
| مصطلحات موضوعية: |
indoor inertial positioning, MEMS-IMU, improved pedestrian dead reckoning, robust adaptive Kalman filter, Chemical technology, TP1-1185 |
| الوصف: |
Pedestrian dead reckoning (PDR) systems based on a microelectromechanical-inertial measurement unit (MEMS-IMU) providing advantages of full autonomy and strong anti-jamming performance are becoming a feasible choice for pedestrian indoor positioning. In order to realize the accurate positioning of pedestrians in a closed environment, an improved pedestrian dead reckoning algorithm, mainly including improved step estimation and heading estimation, is proposed in this paper. Firstly, the original signal is preprocessed using the wavelet denoising algorithm. Then, the multi-threshold method is proposed to ameliorate the step estimation algorithm. For heading estimation suffering from accumulated error and outliers, robust adaptive Kalman filter (RAKF) algorithm is proposed in this paper, and combined with complementary filter to improve positioning accuracy. Finally, an experimental platform with inertial sensors as the core is constructed. Experimental results show that positioning error is less than 2.5% of the total distance, which is ideal for accurate positioning of pedestrians in enclosed environment. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
1424-8220 |
| Relation: |
http://www.mdpi.com/1424-8220/19/2/294; https://doaj.org/toc/1424-8220 |
| DOI: |
10.3390/s19020294 |
| URL الوصول: |
https://doaj.org/article/8fe70c815ca74ecabdc4be45144c6b89 |
| رقم الانضمام: |
edsdoj.8fe70c815ca74ecabdc4be45144c6b89 |
| قاعدة البيانات: |
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