A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length
A smartphone equipped with a Global Navigation Satellite System (GNSS) module can generate positional information for location-based services. However, GNSS signals are susceptible to fragility, multipath (MP), and Non-Line-Of-Sight (NLOS) interference, which can lead to a degradation in the accurac...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/20/4993 |
| _version_ | 1797572407905484800 |
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| author | Changhui Jiang Yuwei Chen Zuoya Liu Qingyuan Xia Chen Chen Juha Hyyppa |
| author_facet | Changhui Jiang Yuwei Chen Zuoya Liu Qingyuan Xia Chen Chen Juha Hyyppa |
| author_sort | Changhui Jiang |
| collection | DOAJ |
| description | A smartphone equipped with a Global Navigation Satellite System (GNSS) module can generate positional information for location-based services. However, GNSS signals are susceptible to fragility, multipath (MP), and Non-Line-Of-Sight (NLOS) interference, which can lead to a degradation in the accuracy of GNSS positioning on smartphones. Due to limitations in the smartphone’s antenna, GNSS signal strength is typically lower. Moreover, in urban areas, where smartphones rely on GNSS, MP and NLOS signals are the primary factors impeding accurate positioning. In this paper, with the goal of enhancing both the accuracy and robustness of smartphone GNSS positioning, we propose two methods. Firstly, an optimized particle filter method employing a Krill Herd Algorithm (KHA) is suggested for the integration of GNSS and Pedestrian Dead Reckoning (PDR). Secondly, a probabilistic approach is presented to identify faulty GNSS measurements using step distance information obtained from the PDR. Experimental tests were conducted using smartphones to evaluate the performance of the proposed method. The results demonstrate that both the KHA and fault detection methods effectively enhance the performance of integrated PDR and GNSS. |
| first_indexed | 2024-03-10T20:55:50Z |
| format | Article |
| id | doaj.art-96bb76e878914c7fac5ddbb9b4e72f1f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:55:50Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-96bb76e878914c7fac5ddbb9b4e72f1f2023-11-19T17:59:20ZengMDPI AGRemote Sensing2072-42922023-10-011520499310.3390/rs15204993A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step LengthChanghui Jiang0Yuwei Chen1Zuoya Liu2Qingyuan Xia3Chen Chen4Juha Hyyppa5College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaLaboratory of Advanced Laser Technology of Anhui Province, Hefei 230037, ChinaDepartment of Photogrammetry and Remote Sensing, Finnish Geospatial Research Institute, 02150 Espoo, FinlandSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaLaboratory of Advanced Laser Technology of Anhui Province, Hefei 230037, ChinaLaboratory of Advanced Laser Technology of Anhui Province, Hefei 230037, ChinaA smartphone equipped with a Global Navigation Satellite System (GNSS) module can generate positional information for location-based services. However, GNSS signals are susceptible to fragility, multipath (MP), and Non-Line-Of-Sight (NLOS) interference, which can lead to a degradation in the accuracy of GNSS positioning on smartphones. Due to limitations in the smartphone’s antenna, GNSS signal strength is typically lower. Moreover, in urban areas, where smartphones rely on GNSS, MP and NLOS signals are the primary factors impeding accurate positioning. In this paper, with the goal of enhancing both the accuracy and robustness of smartphone GNSS positioning, we propose two methods. Firstly, an optimized particle filter method employing a Krill Herd Algorithm (KHA) is suggested for the integration of GNSS and Pedestrian Dead Reckoning (PDR). Secondly, a probabilistic approach is presented to identify faulty GNSS measurements using step distance information obtained from the PDR. Experimental tests were conducted using smartphones to evaluate the performance of the proposed method. The results demonstrate that both the KHA and fault detection methods effectively enhance the performance of integrated PDR and GNSS.https://www.mdpi.com/2072-4292/15/20/4993smartphonePDRoutliersGNSS |
| spellingShingle | Changhui Jiang Yuwei Chen Zuoya Liu Qingyuan Xia Chen Chen Juha Hyyppa A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length Remote Sensing smartphone PDR outliers GNSS |
| title | A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length |
| title_full | A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length |
| title_fullStr | A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length |
| title_full_unstemmed | A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length |
| title_short | A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length |
| title_sort | probabilistic method based smartphone gnss fault detection and exclusion system utilizing pdr step length |
| topic | smartphone PDR outliers GNSS |
| url | https://www.mdpi.com/2072-4292/15/20/4993 |
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