GRU–Transformer Hybrid Model for GNSS/INS Integration in Orchard Environments
Precision positioning in orchards relies on Global Navigation Satellite System and Inertial Navigation System (GNSS/INS) integration. However, dense foliage often causes GNSS blockages, degrading accuracy and robustness. This paper proposes an optimized GNSS/INS integrated navigation method based on...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/11/1135 |
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| author | Peng Gao Jinzhen Fang Junlin He Shuang Ma Guanghua Wen Zhen Li |
| author_facet | Peng Gao Jinzhen Fang Junlin He Shuang Ma Guanghua Wen Zhen Li |
| author_sort | Peng Gao |
| collection | DOAJ |
| description | Precision positioning in orchards relies on Global Navigation Satellite System and Inertial Navigation System (GNSS/INS) integration. However, dense foliage often causes GNSS blockages, degrading accuracy and robustness. This paper proposes an optimized GNSS/INS integrated navigation method based on a hybrid Gated Recurrent Unit (GRU)–Transformer model (GRU-T). The GRU–Transformer hybrid dynamically adjusts the process noise covariance matrix within an error-state Extended Kalman Filter (ES-EKF) framework to address non-stationary noise and signal outages. Forest field tests demonstrate that GRU-T significantly improves positioning accuracy. Compared with the conventional ES-EKF, the proposed method achieves reductions in position root mean square error (PRMSE) of 48.74% (East), 41.94% (North), and 61.59% (Up), and reductions in velocity root mean square error (VRMSE) of 71.5% (East), 39.31% (North), and 56.48% (Up) in the East–North–Up (ENU) coordinate frame. The GRU-T model effectively captures both short- and long-term temporal dependencies and meets real-time, high-frequency sampling requirements. These results indicate that the GRU–Transformer hybrid model enhances the accuracy and robustness of GNSS/INS navigation in complex orchard environments, offering technical support for high-precision positioning in intelligent agricultural machinery systems. |
| format | Article |
| id | doaj-art-6b59a70d2e884d58a78811a42f18aa69 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-6b59a70d2e884d58a78811a42f18aa692025-08-20T03:46:47ZengMDPI AGAgriculture2077-04722025-05-011511113510.3390/agriculture15111135GRU–Transformer Hybrid Model for GNSS/INS Integration in Orchard EnvironmentsPeng Gao0Jinzhen Fang1Junlin He2Shuang Ma3Guanghua Wen4Zhen Li5College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaPrecision positioning in orchards relies on Global Navigation Satellite System and Inertial Navigation System (GNSS/INS) integration. However, dense foliage often causes GNSS blockages, degrading accuracy and robustness. This paper proposes an optimized GNSS/INS integrated navigation method based on a hybrid Gated Recurrent Unit (GRU)–Transformer model (GRU-T). The GRU–Transformer hybrid dynamically adjusts the process noise covariance matrix within an error-state Extended Kalman Filter (ES-EKF) framework to address non-stationary noise and signal outages. Forest field tests demonstrate that GRU-T significantly improves positioning accuracy. Compared with the conventional ES-EKF, the proposed method achieves reductions in position root mean square error (PRMSE) of 48.74% (East), 41.94% (North), and 61.59% (Up), and reductions in velocity root mean square error (VRMSE) of 71.5% (East), 39.31% (North), and 56.48% (Up) in the East–North–Up (ENU) coordinate frame. The GRU-T model effectively captures both short- and long-term temporal dependencies and meets real-time, high-frequency sampling requirements. These results indicate that the GRU–Transformer hybrid model enhances the accuracy and robustness of GNSS/INS navigation in complex orchard environments, offering technical support for high-precision positioning in intelligent agricultural machinery systems.https://www.mdpi.com/2077-0472/15/11/1135transformerGRUsensor fusionKalman filtertrajectory predictionorchard navigation |
| spellingShingle | Peng Gao Jinzhen Fang Junlin He Shuang Ma Guanghua Wen Zhen Li GRU–Transformer Hybrid Model for GNSS/INS Integration in Orchard Environments Agriculture transformer GRU sensor fusion Kalman filter trajectory prediction orchard navigation |
| title | GRU–Transformer Hybrid Model for GNSS/INS Integration in Orchard Environments |
| title_full | GRU–Transformer Hybrid Model for GNSS/INS Integration in Orchard Environments |
| title_fullStr | GRU–Transformer Hybrid Model for GNSS/INS Integration in Orchard Environments |
| title_full_unstemmed | GRU–Transformer Hybrid Model for GNSS/INS Integration in Orchard Environments |
| title_short | GRU–Transformer Hybrid Model for GNSS/INS Integration in Orchard Environments |
| title_sort | gru transformer hybrid model for gnss ins integration in orchard environments |
| topic | transformer GRU sensor fusion Kalman filter trajectory prediction orchard navigation |
| url | https://www.mdpi.com/2077-0472/15/11/1135 |
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