SNR-Dependent Environmental Model: Application in Real-Time GNSS Landslide Monitoring
The Global Navigation Satellite System (GNSS) is currently one of the important tools for landslide monitoring and early warning. However, the majority of GNSS devices are installed in mountainous areas and a variety of vegetation. These harsh environments lead to defective signals at high elevation...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/22/5017 |
| _version_ | 1798041375650873344 |
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| author | Junqiang Han Rui Tu Rui Zhang Lihong Fan Pengfei Zhang |
| author_facet | Junqiang Han Rui Tu Rui Zhang Lihong Fan Pengfei Zhang |
| author_sort | Junqiang Han |
| collection | DOAJ |
| description | The Global Navigation Satellite System (GNSS) is currently one of the important tools for landslide monitoring and early warning. However, the majority of GNSS devices are installed in mountainous areas and a variety of vegetation. These harsh environments lead to defective signals at high elevation angles, rendering real-time successive and reliable positioning results for monitoring difficult. In this study, an environmental model derived from signal-to-noise ratio (SNR) is proposed to enhance the precision and convergence time of positioning in harsh environments. A series of experiments are conducted on weighting and ambiguity-fixed models to evaluate performance. The results indicate that the proposed SNR-dependent environment model could lead to a significant improvement in precision and convergence time; with an obtained root mean squared result on the millimeter level, a convergence time of a few seconds, and utilization which could reach 100%, for continuous and reliable positioning results. These results indicate that the proposed SNR-dependent environment model enhances the performance of GNSS monitoring and early warning to provide continuous and reliable positioning results in real-time. |
| first_indexed | 2024-04-11T22:20:40Z |
| format | Article |
| id | doaj.art-0d93e095121e4d73b2300321f100d279 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:20:40Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0d93e095121e4d73b2300321f100d2792022-12-22T04:00:07ZengMDPI AGSensors1424-82202019-11-011922501710.3390/s19225017s19225017SNR-Dependent Environmental Model: Application in Real-Time GNSS Landslide MonitoringJunqiang Han0Rui Tu1Rui Zhang2Lihong Fan3Pengfei Zhang4National Time Service Center, Chinese Academy of Sciences, Shu Yuan Road, Xi’an 710600, ChinaNational Time Service Center, Chinese Academy of Sciences, Shu Yuan Road, Xi’an 710600, ChinaNational Time Service Center, Chinese Academy of Sciences, Shu Yuan Road, Xi’an 710600, ChinaNational Time Service Center, Chinese Academy of Sciences, Shu Yuan Road, Xi’an 710600, ChinaNational Time Service Center, Chinese Academy of Sciences, Shu Yuan Road, Xi’an 710600, ChinaThe Global Navigation Satellite System (GNSS) is currently one of the important tools for landslide monitoring and early warning. However, the majority of GNSS devices are installed in mountainous areas and a variety of vegetation. These harsh environments lead to defective signals at high elevation angles, rendering real-time successive and reliable positioning results for monitoring difficult. In this study, an environmental model derived from signal-to-noise ratio (SNR) is proposed to enhance the precision and convergence time of positioning in harsh environments. A series of experiments are conducted on weighting and ambiguity-fixed models to evaluate performance. The results indicate that the proposed SNR-dependent environment model could lead to a significant improvement in precision and convergence time; with an obtained root mean squared result on the millimeter level, a convergence time of a few seconds, and utilization which could reach 100%, for continuous and reliable positioning results. These results indicate that the proposed SNR-dependent environment model enhances the performance of GNSS monitoring and early warning to provide continuous and reliable positioning results in real-time.https://www.mdpi.com/1424-8220/19/22/5017gnsssnr-dependentenvironment modellandslide monitoring |
| spellingShingle | Junqiang Han Rui Tu Rui Zhang Lihong Fan Pengfei Zhang SNR-Dependent Environmental Model: Application in Real-Time GNSS Landslide Monitoring Sensors gnss snr-dependent environment model landslide monitoring |
| title | SNR-Dependent Environmental Model: Application in Real-Time GNSS Landslide Monitoring |
| title_full | SNR-Dependent Environmental Model: Application in Real-Time GNSS Landslide Monitoring |
| title_fullStr | SNR-Dependent Environmental Model: Application in Real-Time GNSS Landslide Monitoring |
| title_full_unstemmed | SNR-Dependent Environmental Model: Application in Real-Time GNSS Landslide Monitoring |
| title_short | SNR-Dependent Environmental Model: Application in Real-Time GNSS Landslide Monitoring |
| title_sort | snr dependent environmental model application in real time gnss landslide monitoring |
| topic | gnss snr-dependent environment model landslide monitoring |
| url | https://www.mdpi.com/1424-8220/19/22/5017 |
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