WiFi-Aided Magnetic Matching for Indoor Navigation with Consumer Portable Devices
This paper presents a WiFi-aided magnetic matching (MM) algorithm for indoor pedestrian navigation with consumer portable devices. This algorithm reduces both the mismatching rate (i.e., the rate of matching to an incorrect point that is more than 20 m away from the true value) and computational loa...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-06-01
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| Series: | Micromachines |
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| Online Access: | http://www.mdpi.com/2072-666X/6/6/747 |
| _version_ | 1811240448386138112 |
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| author | You Li Yuan Zhuang Haiyu Lan Peng Zhang Xiaoji Niu Naser El-Sheimy |
| author_facet | You Li Yuan Zhuang Haiyu Lan Peng Zhang Xiaoji Niu Naser El-Sheimy |
| author_sort | You Li |
| collection | DOAJ |
| description | This paper presents a WiFi-aided magnetic matching (MM) algorithm for indoor pedestrian navigation with consumer portable devices. This algorithm reduces both the mismatching rate (i.e., the rate of matching to an incorrect point that is more than 20 m away from the true value) and computational load of MM by using WiFi positioning solutions to limit the MM search space. Walking tests with Samsung Galaxy S3 and S4 smartphones in two different indoor environments (i.e., Environment #1 with abundant WiFi APs and significant magnetic features, and Environment #2 with less WiFi and magnetic information) were conducted to evaluate the proposed algorithm. It was found that WiFi fingerprinting accuracy is related to the signal distributions. MM provided results with small fluctuations but had a significant mismatch rate; when aided by WiFi, MM’s robustness was significantly improved. The outcome of this research indicates that WiFi and MM have complementary characteristics as the former is a point-by-point matching approach and the latter is based on profile-matching. Furthermore, performance improvement through integrating WiFi and MM depends on the environment (e.g., the signal distributions of magnetic intensity and WiFi RSS): In Environment #1 tests, WiFi-aided MM and WiFi provided similar results; in Environment #2 tests, the former was approximately 41.6% better. Our results supported that the WiFi-aided MM algorithm provided more reliable solutions than both WiFi and MM in the areas that have poor WiFi signal distribution or indistinctive magnetic-gradient features. |
| first_indexed | 2024-04-12T13:19:17Z |
| format | Article |
| id | doaj.art-2f0fb230dbb3434e9c0d6cd109b0a6b7 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-04-12T13:19:17Z |
| publishDate | 2015-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-2f0fb230dbb3434e9c0d6cd109b0a6b72022-12-22T03:31:31ZengMDPI AGMicromachines2072-666X2015-06-016674776410.3390/mi6060747mi6060747WiFi-Aided Magnetic Matching for Indoor Navigation with Consumer Portable DevicesYou Li0Yuan Zhuang1Haiyu Lan2Peng Zhang3Xiaoji Niu4Naser El-Sheimy5Department 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, CanadaGNSS Research Center, Wuhan University, Wuhan 430079, ChinaGNSS Research Center, Wuhan University, Wuhan 430079, ChinaDepartment of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaThis paper presents a WiFi-aided magnetic matching (MM) algorithm for indoor pedestrian navigation with consumer portable devices. This algorithm reduces both the mismatching rate (i.e., the rate of matching to an incorrect point that is more than 20 m away from the true value) and computational load of MM by using WiFi positioning solutions to limit the MM search space. Walking tests with Samsung Galaxy S3 and S4 smartphones in two different indoor environments (i.e., Environment #1 with abundant WiFi APs and significant magnetic features, and Environment #2 with less WiFi and magnetic information) were conducted to evaluate the proposed algorithm. It was found that WiFi fingerprinting accuracy is related to the signal distributions. MM provided results with small fluctuations but had a significant mismatch rate; when aided by WiFi, MM’s robustness was significantly improved. The outcome of this research indicates that WiFi and MM have complementary characteristics as the former is a point-by-point matching approach and the latter is based on profile-matching. Furthermore, performance improvement through integrating WiFi and MM depends on the environment (e.g., the signal distributions of magnetic intensity and WiFi RSS): In Environment #1 tests, WiFi-aided MM and WiFi provided similar results; in Environment #2 tests, the former was approximately 41.6% better. Our results supported that the WiFi-aided MM algorithm provided more reliable solutions than both WiFi and MM in the areas that have poor WiFi signal distribution or indistinctive magnetic-gradient features.http://www.mdpi.com/2072-666X/6/6/747pedestrian navigationsmartphonesindoor positioningMEMS sensorsWiFi fingerprintingmagnetic matching |
| spellingShingle | You Li Yuan Zhuang Haiyu Lan Peng Zhang Xiaoji Niu Naser El-Sheimy WiFi-Aided Magnetic Matching for Indoor Navigation with Consumer Portable Devices Micromachines pedestrian navigation smartphones indoor positioning MEMS sensors WiFi fingerprinting magnetic matching |
| title | WiFi-Aided Magnetic Matching for Indoor Navigation with Consumer Portable Devices |
| title_full | WiFi-Aided Magnetic Matching for Indoor Navigation with Consumer Portable Devices |
| title_fullStr | WiFi-Aided Magnetic Matching for Indoor Navigation with Consumer Portable Devices |
| title_full_unstemmed | WiFi-Aided Magnetic Matching for Indoor Navigation with Consumer Portable Devices |
| title_short | WiFi-Aided Magnetic Matching for Indoor Navigation with Consumer Portable Devices |
| title_sort | wifi aided magnetic matching for indoor navigation with consumer portable devices |
| topic | pedestrian navigation smartphones indoor positioning MEMS sensors WiFi fingerprinting magnetic matching |
| url | http://www.mdpi.com/2072-666X/6/6/747 |
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