GRU–Transformer Hybrid Model for GNSS/INS Integration in Orchard Environments

Peng Gao, Jinzhen Fang, Junlin He, Shuang Ma, Guanghua Wen and Zhen Li ()
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Peng Gao: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Jinzhen Fang: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Junlin He: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Shuang Ma: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Guanghua Wen: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Zhen Li: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China

Agriculture, 2025, vol. 15, issue 11, 1-23

Abstract: 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.

Keywords: transformer; GRU; sensor fusion; Kalman filter; trajectory prediction; orchard navigation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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