Improved Pedestrian Positioning with Inertial Sensor Based on Adaptive Gradient Descent and Double-Constrained Extended Kalman Filter
The Foot-mounted Inertial Pedestrian-Positioning System (FIPPS) based on the Micro-Inertial Measurement Unit (MIMU) is a good choice for the forest fire fighters when the Global Navigation Satellite System is unavailable. Zero Velocity Update (ZUPT) provides a solution for reducing cumulative positi...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/4361812 |
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| author | Miaoxin Ji Jinhao Liu Xiangbo Xu Yuyang Guo Zhenchun Lu |
| author_facet | Miaoxin Ji Jinhao Liu Xiangbo Xu Yuyang Guo Zhenchun Lu |
| author_sort | Miaoxin Ji |
| collection | DOAJ |
| description | The Foot-mounted Inertial Pedestrian-Positioning System (FIPPS) based on the Micro-Inertial Measurement Unit (MIMU) is a good choice for the forest fire fighters when the Global Navigation Satellite System is unavailable. Zero Velocity Update (ZUPT) provides a solution for reducing cumulative positioning errors caused by the integral calculation of the inertial navigation. However, the performance of ZUPT is highly affected by the low accuracy and high noise of the MIMU. The accuracy of conventional ZUPT for attitude alignment is reduced by the zero offset of acceleration and the drift of a gyroscope during the standing phase. An initial alignment algorithm based on Adaptive Gradient Descent Algorithm (AGDA) is proposed. In the stepping phase, the extended Kalman filter (EKF) is often used to correct attitude and position in track estimation. However, the measurement noise of the EKF is influenced by the high-frequency acceleration and angular velocity. Thus, the accuracy of the attitude and position will decrease. A double-constrained extended Kalman filtering (DEKF) is proposed. An adaptive parameter positively correlated with the acceleration and angular velocity is set, and the measurement noise in the DEKF is adaptively adjusted. The performance of the proposed method is verified by implementing the pedestrian test trajectory using MPU-9150 MIMU manufactured by InvenSense. The results show that the attitude error of the AGDA is 33.82% less than that of the conventional GDA. The attitude error of DEKF is 21.70% less than that of the conventional EKF. The experimental results verify the effectiveness and applicability of the proposed method. |
| format | Article |
| id | doaj-art-70ff40f1210a45b287d04cb1399defcf |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-70ff40f1210a45b287d04cb1399defcf2025-02-03T05:59:35ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/43618124361812Improved Pedestrian Positioning with Inertial Sensor Based on Adaptive Gradient Descent and Double-Constrained Extended Kalman FilterMiaoxin Ji0Jinhao Liu1Xiangbo Xu2Yuyang Guo3Zhenchun Lu4School of Technology, Beijing Forestry University, No. 35 Tsinghua East Road, Haidian District, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, No. 35 Tsinghua East Road, Haidian District, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, No. 35 Tsinghua East Road, Haidian District, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, No. 35 Tsinghua East Road, Haidian District, Beijing 100083, ChinaSchool of Technology, Beijing Forestry University, No. 35 Tsinghua East Road, Haidian District, Beijing 100083, ChinaThe Foot-mounted Inertial Pedestrian-Positioning System (FIPPS) based on the Micro-Inertial Measurement Unit (MIMU) is a good choice for the forest fire fighters when the Global Navigation Satellite System is unavailable. Zero Velocity Update (ZUPT) provides a solution for reducing cumulative positioning errors caused by the integral calculation of the inertial navigation. However, the performance of ZUPT is highly affected by the low accuracy and high noise of the MIMU. The accuracy of conventional ZUPT for attitude alignment is reduced by the zero offset of acceleration and the drift of a gyroscope during the standing phase. An initial alignment algorithm based on Adaptive Gradient Descent Algorithm (AGDA) is proposed. In the stepping phase, the extended Kalman filter (EKF) is often used to correct attitude and position in track estimation. However, the measurement noise of the EKF is influenced by the high-frequency acceleration and angular velocity. Thus, the accuracy of the attitude and position will decrease. A double-constrained extended Kalman filtering (DEKF) is proposed. An adaptive parameter positively correlated with the acceleration and angular velocity is set, and the measurement noise in the DEKF is adaptively adjusted. The performance of the proposed method is verified by implementing the pedestrian test trajectory using MPU-9150 MIMU manufactured by InvenSense. The results show that the attitude error of the AGDA is 33.82% less than that of the conventional GDA. The attitude error of DEKF is 21.70% less than that of the conventional EKF. The experimental results verify the effectiveness and applicability of the proposed method.http://dx.doi.org/10.1155/2020/4361812 |
| spellingShingle | Miaoxin Ji Jinhao Liu Xiangbo Xu Yuyang Guo Zhenchun Lu Improved Pedestrian Positioning with Inertial Sensor Based on Adaptive Gradient Descent and Double-Constrained Extended Kalman Filter Complexity |
| title | Improved Pedestrian Positioning with Inertial Sensor Based on Adaptive Gradient Descent and Double-Constrained Extended Kalman Filter |
| title_full | Improved Pedestrian Positioning with Inertial Sensor Based on Adaptive Gradient Descent and Double-Constrained Extended Kalman Filter |
| title_fullStr | Improved Pedestrian Positioning with Inertial Sensor Based on Adaptive Gradient Descent and Double-Constrained Extended Kalman Filter |
| title_full_unstemmed | Improved Pedestrian Positioning with Inertial Sensor Based on Adaptive Gradient Descent and Double-Constrained Extended Kalman Filter |
| title_short | Improved Pedestrian Positioning with Inertial Sensor Based on Adaptive Gradient Descent and Double-Constrained Extended Kalman Filter |
| title_sort | improved pedestrian positioning with inertial sensor based on adaptive gradient descent and double constrained extended kalman filter |
| url | http://dx.doi.org/10.1155/2020/4361812 |
| work_keys_str_mv | AT miaoxinji improvedpedestrianpositioningwithinertialsensorbasedonadaptivegradientdescentanddoubleconstrainedextendedkalmanfilter AT jinhaoliu improvedpedestrianpositioningwithinertialsensorbasedonadaptivegradientdescentanddoubleconstrainedextendedkalmanfilter AT xiangboxu improvedpedestrianpositioningwithinertialsensorbasedonadaptivegradientdescentanddoubleconstrainedextendedkalmanfilter AT yuyangguo improvedpedestrianpositioningwithinertialsensorbasedonadaptivegradientdescentanddoubleconstrainedextendedkalmanfilter AT zhenchunlu improvedpedestrianpositioningwithinertialsensorbasedonadaptivegradientdescentanddoubleconstrainedextendedkalmanfilter |