Improved Pedestrian Dead Reckoning Based on a Robust Adaptive Kalman Filter for Indoor Inertial Location System
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 p...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/19/2/294 |
| _version_ | 1828747618974433280 |
|---|---|
| author | Qigao Fan Hai Zhang Peng Pan Xiangpeng Zhuang Jie Jia Pengsong Zhang Zhengqing Zhao Gaowen Zhu Yuanyuan Tang |
| author_facet | Qigao Fan Hai Zhang Peng Pan Xiangpeng Zhuang Jie Jia Pengsong Zhang Zhengqing Zhao Gaowen Zhu Yuanyuan Tang |
| author_sort | Qigao Fan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T04:44:38Z |
| format | Article |
| id | doaj.art-8fe70c815ca74ecabdc4be45144c6b89 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T04:44:38Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8fe70c815ca74ecabdc4be45144c6b892022-12-22T02:11:29ZengMDPI AGSensors1424-82202019-01-0119229410.3390/s19020294s19020294Improved Pedestrian Dead Reckoning Based on a Robust Adaptive Kalman Filter for Indoor Inertial Location SystemQigao Fan0Hai Zhang1Peng Pan2Xiangpeng Zhuang3Jie Jia4Pengsong Zhang5Zhengqing Zhao6Gaowen Zhu7Yuanyuan Tang8Internet of Things Engineering, Jiangnan University, Wuxi 214000, ChinaInternet of Things Engineering, Jiangnan University, Wuxi 214000, ChinaDepartment of Mechanical Engineering, McGill University, Montreal, QC H3A 0G4, CanadaInternet of Things Engineering, Jiangnan University, Wuxi 214000, ChinaInternet of Things Engineering, Jiangnan University, Wuxi 214000, ChinaInternet of Things Engineering, Jiangnan University, Wuxi 214000, ChinaInternet of Things Engineering, Jiangnan University, Wuxi 214000, ChinaInternet of Things Engineering, Jiangnan University, Wuxi 214000, ChinaInternet of Things Engineering, Jiangnan University, Wuxi 214000, ChinaPedestrian 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.http://www.mdpi.com/1424-8220/19/2/294indoor inertial positioningMEMS-IMUimproved pedestrian dead reckoningrobust adaptive Kalman filter |
| spellingShingle | Qigao Fan Hai Zhang Peng Pan Xiangpeng Zhuang Jie Jia Pengsong Zhang Zhengqing Zhao Gaowen Zhu Yuanyuan Tang Improved Pedestrian Dead Reckoning Based on a Robust Adaptive Kalman Filter for Indoor Inertial Location System Sensors indoor inertial positioning MEMS-IMU improved pedestrian dead reckoning robust adaptive Kalman filter |
| title | Improved Pedestrian Dead Reckoning Based on a Robust Adaptive Kalman Filter for Indoor Inertial Location System |
| title_full | Improved Pedestrian Dead Reckoning Based on a Robust Adaptive Kalman Filter for Indoor Inertial Location System |
| title_fullStr | Improved Pedestrian Dead Reckoning Based on a Robust Adaptive Kalman Filter for Indoor Inertial Location System |
| title_full_unstemmed | Improved Pedestrian Dead Reckoning Based on a Robust Adaptive Kalman Filter for Indoor Inertial Location System |
| title_short | Improved Pedestrian Dead Reckoning Based on a Robust Adaptive Kalman Filter for Indoor Inertial Location System |
| title_sort | improved pedestrian dead reckoning based on a robust adaptive kalman filter for indoor inertial location system |
| topic | indoor inertial positioning MEMS-IMU improved pedestrian dead reckoning robust adaptive Kalman filter |
| url | http://www.mdpi.com/1424-8220/19/2/294 |
| work_keys_str_mv | AT qigaofan improvedpedestriandeadreckoningbasedonarobustadaptivekalmanfilterforindoorinertiallocationsystem AT haizhang improvedpedestriandeadreckoningbasedonarobustadaptivekalmanfilterforindoorinertiallocationsystem AT pengpan improvedpedestriandeadreckoningbasedonarobustadaptivekalmanfilterforindoorinertiallocationsystem AT xiangpengzhuang improvedpedestriandeadreckoningbasedonarobustadaptivekalmanfilterforindoorinertiallocationsystem AT jiejia improvedpedestriandeadreckoningbasedonarobustadaptivekalmanfilterforindoorinertiallocationsystem AT pengsongzhang improvedpedestriandeadreckoningbasedonarobustadaptivekalmanfilterforindoorinertiallocationsystem AT zhengqingzhao improvedpedestriandeadreckoningbasedonarobustadaptivekalmanfilterforindoorinertiallocationsystem AT gaowenzhu improvedpedestriandeadreckoningbasedonarobustadaptivekalmanfilterforindoorinertiallocationsystem AT yuanyuantang improvedpedestriandeadreckoningbasedonarobustadaptivekalmanfilterforindoorinertiallocationsystem |