Carrying Position Independent User Heading Estimation for Indoor Pedestrian Navigation with Smartphones
This paper proposes a novel heading estimation approach for indoor pedestrian navigation using the built-in inertial sensors on a smartphone. Unlike previous approaches constraining the carrying position of a smartphone on the user’s body, our approach gives the user a larger freedom by implementing...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/16/5/677 |
| _version_ | 1811278810757201920 |
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| author | Zhi-An Deng Guofeng Wang Ying Hu Yang Cui |
| author_facet | Zhi-An Deng Guofeng Wang Ying Hu Yang Cui |
| author_sort | Zhi-An Deng |
| collection | DOAJ |
| description | This paper proposes a novel heading estimation approach for indoor pedestrian navigation using the built-in inertial sensors on a smartphone. Unlike previous approaches constraining the carrying position of a smartphone on the user’s body, our approach gives the user a larger freedom by implementing automatic recognition of the device carrying position and subsequent selection of an optimal strategy for heading estimation. We firstly predetermine the motion state by a decision tree using an accelerometer and a barometer. Then, to enable accurate and computational lightweight carrying position recognition, we combine a position classifier with a novel position transition detection algorithm, which may also be used to avoid the confusion between position transition and user turn during pedestrian walking. For a device placed in the trouser pockets or held in a swinging hand, the heading estimation is achieved by deploying a principal component analysis (PCA)-based approach. For a device held in the hand or against the ear during a phone call, user heading is directly estimated by adding the yaw angle of the device to the related heading offset. Experimental results show that our approach can automatically detect carrying positions with high accuracy, and outperforms previous heading estimation approaches in terms of accuracy and applicability. |
| first_indexed | 2024-04-13T00:42:15Z |
| format | Article |
| id | doaj.art-9184f0b68edd4942b3ff256dcc603f11 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T00:42:15Z |
| publishDate | 2016-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9184f0b68edd4942b3ff256dcc603f112022-12-22T03:10:07ZengMDPI AGSensors1424-82202016-05-0116567710.3390/s16050677s16050677Carrying Position Independent User Heading Estimation for Indoor Pedestrian Navigation with SmartphonesZhi-An Deng0Guofeng Wang1Ying Hu2Yang Cui3School of Information Science and Technology, Dalian Maritime University, Dalian 116026, ChinaSchool of Information Science and Technology, Dalian Maritime University, Dalian 116026, ChinaSchool of Information Science and Technology, Dalian Maritime University, Dalian 116026, ChinaSchool of computer science and technology, Harbin Institute of Technology, Harbin 150001, ChinaThis paper proposes a novel heading estimation approach for indoor pedestrian navigation using the built-in inertial sensors on a smartphone. Unlike previous approaches constraining the carrying position of a smartphone on the user’s body, our approach gives the user a larger freedom by implementing automatic recognition of the device carrying position and subsequent selection of an optimal strategy for heading estimation. We firstly predetermine the motion state by a decision tree using an accelerometer and a barometer. Then, to enable accurate and computational lightweight carrying position recognition, we combine a position classifier with a novel position transition detection algorithm, which may also be used to avoid the confusion between position transition and user turn during pedestrian walking. For a device placed in the trouser pockets or held in a swinging hand, the heading estimation is achieved by deploying a principal component analysis (PCA)-based approach. For a device held in the hand or against the ear during a phone call, user heading is directly estimated by adding the yaw angle of the device to the related heading offset. Experimental results show that our approach can automatically detect carrying positions with high accuracy, and outperforms previous heading estimation approaches in terms of accuracy and applicability.http://www.mdpi.com/1424-8220/16/5/677heading estimationcarrying positioninertial sensorsprincipal component analysis |
| spellingShingle | Zhi-An Deng Guofeng Wang Ying Hu Yang Cui Carrying Position Independent User Heading Estimation for Indoor Pedestrian Navigation with Smartphones Sensors heading estimation carrying position inertial sensors principal component analysis |
| title | Carrying Position Independent User Heading Estimation for Indoor Pedestrian Navigation with Smartphones |
| title_full | Carrying Position Independent User Heading Estimation for Indoor Pedestrian Navigation with Smartphones |
| title_fullStr | Carrying Position Independent User Heading Estimation for Indoor Pedestrian Navigation with Smartphones |
| title_full_unstemmed | Carrying Position Independent User Heading Estimation for Indoor Pedestrian Navigation with Smartphones |
| title_short | Carrying Position Independent User Heading Estimation for Indoor Pedestrian Navigation with Smartphones |
| title_sort | carrying position independent user heading estimation for indoor pedestrian navigation with smartphones |
| topic | heading estimation carrying position inertial sensors principal component analysis |
| url | http://www.mdpi.com/1424-8220/16/5/677 |
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