An indoor positioning system using pedestrian dead reckoning with WiFi and map-matching aided
In this paper, an indoor positioning system of pedestrian dead reckoning (PDR), with WiFi fingerprint and map-matching techniques, is proposed on a smartphone. Based on five different holding styles, which are classified by using decision tree method, the proposed system supports the user in a more...
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| Định dạng: | Conference paper |
| Ngôn ngữ: | English |
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IEEE
2023
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oai:scholar.dlu.edu.vn:123456789-24302023-06-09T07:03:52Z An indoor positioning system using pedestrian dead reckoning with WiFi and map-matching aided Nguyễn, Hữu Khánh Lee, K. Lee, S. W. Indoor Positioning System In this paper, an indoor positioning system of pedestrian dead reckoning (PDR), with WiFi fingerprint and map-matching techniques, is proposed on a smartphone. Based on five different holding styles, which are classified by using decision tree method, the proposed system supports the user in a more freedom of holding style while walking but still be able to track the location of the user accurately. To compensate the accumulating error of the PDR system, WiFi fingerprint and map-matching techniques are applied. The proposed method aims to enhance the tracking performance of the whole system with the WiFi fingerprint technique as well as can reduce the building cost of the radio map with fewer number of reference positions compared to conventional systems. In addition, the methods to detect turning behaviour and collisions based on a given map information are suggested to correct the position from the PDR system. From numerous experiments, the performance of the whole system is demonstrated. 2023-06-05T08:14:44Z 2023-06-05T08:14:44Z 2017 Conference paper Bài báo đăng trên tạp chí thuộc SCOPUS, bao gồm book chapter https://scholar.dlu.edu.vn/handle/123456789/2430 en 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN) IEEE |
| institution |
Thư viện Trường Đại học Đà Lạt |
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Thư viện số |
| language |
English |
| topic |
Indoor Positioning System |
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Indoor Positioning System Nguyễn, Hữu Khánh Lee, K. Lee, S. W. An indoor positioning system using pedestrian dead reckoning with WiFi and map-matching aided |
| description |
In this paper, an indoor positioning system of pedestrian dead reckoning (PDR), with WiFi fingerprint and map-matching techniques, is proposed on a smartphone. Based on five different holding styles, which are classified by using decision tree method, the proposed system supports the user in a more freedom of holding style while walking but still be able to track the location of the user accurately. To compensate the accumulating error of the PDR system, WiFi fingerprint and map-matching techniques are applied. The proposed method aims to enhance the tracking performance of the whole system with the WiFi fingerprint technique as well as can reduce the building cost of the radio map with fewer number of reference positions compared to conventional systems. In addition, the methods to detect turning behaviour and collisions based on a given map information are suggested to correct the position from the PDR system. From numerous experiments, the performance of the whole system is demonstrated. |
| format |
Conference paper |
| author |
Nguyễn, Hữu Khánh Lee, K. Lee, S. W. |
| author_facet |
Nguyễn, Hữu Khánh Lee, K. Lee, S. W. |
| author_sort |
Nguyễn, Hữu Khánh |
| title |
An indoor positioning system using pedestrian dead reckoning with WiFi and map-matching aided |
| title_short |
An indoor positioning system using pedestrian dead reckoning with WiFi and map-matching aided |
| title_full |
An indoor positioning system using pedestrian dead reckoning with WiFi and map-matching aided |
| title_fullStr |
An indoor positioning system using pedestrian dead reckoning with WiFi and map-matching aided |
| title_full_unstemmed |
An indoor positioning system using pedestrian dead reckoning with WiFi and map-matching aided |
| title_sort |
indoor positioning system using pedestrian dead reckoning with wifi and map-matching aided |
| publisher |
IEEE |
| publishDate |
2023 |
| url |
https://scholar.dlu.edu.vn/handle/123456789/2430 |
| _version_ |
1768306431208980480 |