A Meteorological Data-Driven eLoran Signal Propagation Delay Prediction Model: BP Neural Network Modeling for Long-Distance Scenarios
The timing accuracy of eLoran systems is susceptible to meteorological fluctuations, with medium-to-long-range propagation delay variations reaching hundreds of nanoseconds to microseconds. While conventional models have been widely adopted for short-range delay prediction, they fail to accurately c...
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2269 |
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| author | Tao Jin Shiyao Liu Baorong Yan Wei Guo Changjiang Huang Yu Hua Shougang Zhang Xiaohui Li Lu Xu |
| author_facet | Tao Jin Shiyao Liu Baorong Yan Wei Guo Changjiang Huang Yu Hua Shougang Zhang Xiaohui Li Lu Xu |
| author_sort | Tao Jin |
| collection | DOAJ |
| description | The timing accuracy of eLoran systems is susceptible to meteorological fluctuations, with medium-to-long-range propagation delay variations reaching hundreds of nanoseconds to microseconds. While conventional models have been widely adopted for short-range delay prediction, they fail to accurately characterize the coupled effects of multiple factors in long-range scenarios. This study theoretically examines the influence mechanisms of temperature, humidity, and atmospheric pressure on signal propagation delays, proposing a hybrid prediction model integrating meteorological data with a back-propagation neural network (BPNN) through path-weighted Pearson correlation coefficient analysis. Long-term observational data from multiple differential reference stations and meteorological stations reveal that short-term delay fluctuations strongly correlate with localized instantaneous humidity variations, whereas long-term trends are governed by cumulative temperature–humidity effects in regional environments. A multi-tier neural network architecture was developed, incorporating spatial analysis of propagation distance impacts on model accuracy. Experimental results demonstrate enhanced prediction stability in long-range scenarios. The proposed model provides an innovative tool for eLoran system delay correction, while establishing an interdisciplinary framework that bridges meteorological parameters with signal propagation characteristics. This methodology offers new perspectives for reliable timing solutions in global navigation satellite system (GNSS)-denied environments and advances our understanding of meteorological–electromagnetic wave interactions. |
| format | Article |
| id | doaj-art-b79e2e88e7c24be99ddf7b83cb50a3d6 |
| institution | Matheson Library |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-b79e2e88e7c24be99ddf7b83cb50a3d62025-07-11T14:42:33ZengMDPI AGRemote Sensing2072-42922025-07-011713226910.3390/rs17132269A Meteorological Data-Driven eLoran Signal Propagation Delay Prediction Model: BP Neural Network Modeling for Long-Distance ScenariosTao Jin0Shiyao Liu1Baorong Yan2Wei Guo3Changjiang Huang4Yu Hua5Shougang Zhang6Xiaohui Li7Lu Xu8National Time Service Center, Chinese Academy of Sciences, Xi’an 710600, ChinaNational Time Service Center, Chinese Academy of Sciences, Xi’an 710600, ChinaNational Time Service Center, Chinese Academy of Sciences, Xi’an 710600, ChinaNational Time Service Center, Chinese Academy of Sciences, Xi’an 710600, ChinaNational Time Service Center, Chinese Academy of Sciences, Xi’an 710600, ChinaNational Time Service Center, Chinese Academy of Sciences, Xi’an 710600, ChinaNational Time Service Center, Chinese Academy of Sciences, Xi’an 710600, ChinaNational Time Service Center, Chinese Academy of Sciences, Xi’an 710600, ChinaSchool of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, ChinaThe timing accuracy of eLoran systems is susceptible to meteorological fluctuations, with medium-to-long-range propagation delay variations reaching hundreds of nanoseconds to microseconds. While conventional models have been widely adopted for short-range delay prediction, they fail to accurately characterize the coupled effects of multiple factors in long-range scenarios. This study theoretically examines the influence mechanisms of temperature, humidity, and atmospheric pressure on signal propagation delays, proposing a hybrid prediction model integrating meteorological data with a back-propagation neural network (BPNN) through path-weighted Pearson correlation coefficient analysis. Long-term observational data from multiple differential reference stations and meteorological stations reveal that short-term delay fluctuations strongly correlate with localized instantaneous humidity variations, whereas long-term trends are governed by cumulative temperature–humidity effects in regional environments. A multi-tier neural network architecture was developed, incorporating spatial analysis of propagation distance impacts on model accuracy. Experimental results demonstrate enhanced prediction stability in long-range scenarios. The proposed model provides an innovative tool for eLoran system delay correction, while establishing an interdisciplinary framework that bridges meteorological parameters with signal propagation characteristics. This methodology offers new perspectives for reliable timing solutions in global navigation satellite system (GNSS)-denied environments and advances our understanding of meteorological–electromagnetic wave interactions.https://www.mdpi.com/2072-4292/17/13/2269eLoranmeteorological datatime-delay predictionback propagation neural networklong-distance scenarios |
| spellingShingle | Tao Jin Shiyao Liu Baorong Yan Wei Guo Changjiang Huang Yu Hua Shougang Zhang Xiaohui Li Lu Xu A Meteorological Data-Driven eLoran Signal Propagation Delay Prediction Model: BP Neural Network Modeling for Long-Distance Scenarios Remote Sensing eLoran meteorological data time-delay prediction back propagation neural network long-distance scenarios |
| title | A Meteorological Data-Driven eLoran Signal Propagation Delay Prediction Model: BP Neural Network Modeling for Long-Distance Scenarios |
| title_full | A Meteorological Data-Driven eLoran Signal Propagation Delay Prediction Model: BP Neural Network Modeling for Long-Distance Scenarios |
| title_fullStr | A Meteorological Data-Driven eLoran Signal Propagation Delay Prediction Model: BP Neural Network Modeling for Long-Distance Scenarios |
| title_full_unstemmed | A Meteorological Data-Driven eLoran Signal Propagation Delay Prediction Model: BP Neural Network Modeling for Long-Distance Scenarios |
| title_short | A Meteorological Data-Driven eLoran Signal Propagation Delay Prediction Model: BP Neural Network Modeling for Long-Distance Scenarios |
| title_sort | meteorological data driven eloran signal propagation delay prediction model bp neural network modeling for long distance scenarios |
| topic | eLoran meteorological data time-delay prediction back propagation neural network long-distance scenarios |
| url | https://www.mdpi.com/2072-4292/17/13/2269 |
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