Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter
In this research, a non-infrastructure-based and low-cost indoor navigation method is proposed through the integration of smartphone built-in microelectromechanical systems (MEMS) sensors and indoor map information using an auxiliary particle filter (APF). A cascade structure Kalman particle filter...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-07-01
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| Series: | Micromachines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-666X/8/7/225 |
| _version_ | 1818180716567986176 |
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| author | Chunyang Yu Naser El-Sheimy Haiyu Lan Zhenbo Liu |
| author_facet | Chunyang Yu Naser El-Sheimy Haiyu Lan Zhenbo Liu |
| author_sort | Chunyang Yu |
| collection | DOAJ |
| description | In this research, a non-infrastructure-based and low-cost indoor navigation method is proposed through the integration of smartphone built-in microelectromechanical systems (MEMS) sensors and indoor map information using an auxiliary particle filter (APF). A cascade structure Kalman particle filter algorithm is designed to reduce the computational burden and improve the estimation speed of the APF by decreasing its update frequency and the number of particles used in this research. In the lower filter (Kalman filter), zero velocity update and non-holonomic constraints are used to correct the error of the inertial navigation-derived solutions. The innovation of the design lies in the combination of upper filter (particle filter) map-matching and map-aiding methods to further constrain the navigation solutions. This proposed navigation method simplifies indoor positioning and makes it accessible to individual and group users, while guaranteeing the system’s accuracy. The availability and accuracy of the proposed algorithm are tested and validated through experiments in various practical scenarios. |
| first_indexed | 2024-12-11T21:24:11Z |
| format | Article |
| id | doaj.art-ed05ead470c048f2a36fc26b113d254f |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-12-11T21:24:11Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-ed05ead470c048f2a36fc26b113d254f2022-12-22T00:50:22ZengMDPI AGMicromachines2072-666X2017-07-018722510.3390/mi8070225mi8070225Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle FilterChunyang Yu0Naser El-Sheimy1Haiyu Lan2Zhenbo Liu3College of Automation, Haibin Engineering University, Harbin 150001, ChinaDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaIn this research, a non-infrastructure-based and low-cost indoor navigation method is proposed through the integration of smartphone built-in microelectromechanical systems (MEMS) sensors and indoor map information using an auxiliary particle filter (APF). A cascade structure Kalman particle filter algorithm is designed to reduce the computational burden and improve the estimation speed of the APF by decreasing its update frequency and the number of particles used in this research. In the lower filter (Kalman filter), zero velocity update and non-holonomic constraints are used to correct the error of the inertial navigation-derived solutions. The innovation of the design lies in the combination of upper filter (particle filter) map-matching and map-aiding methods to further constrain the navigation solutions. This proposed navigation method simplifies indoor positioning and makes it accessible to individual and group users, while guaranteeing the system’s accuracy. The availability and accuracy of the proposed algorithm are tested and validated through experiments in various practical scenarios.https://www.mdpi.com/2072-666X/8/7/225map informationMEMS sensorsmap aidingmap matchingauxiliary particle filtercascade structure algorithmindoor pedestrian navigation |
| spellingShingle | Chunyang Yu Naser El-Sheimy Haiyu Lan Zhenbo Liu Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter Micromachines map information MEMS sensors map aiding map matching auxiliary particle filter cascade structure algorithm indoor pedestrian navigation |
| title | Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter |
| title_full | Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter |
| title_fullStr | Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter |
| title_full_unstemmed | Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter |
| title_short | Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter |
| title_sort | map based indoor pedestrian navigation using an auxiliary particle filter |
| topic | map information MEMS sensors map aiding map matching auxiliary particle filter cascade structure algorithm indoor pedestrian navigation |
| url | https://www.mdpi.com/2072-666X/8/7/225 |
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