Adaptive compensation method for navigation positioning errors considering the vibration characteristics of a combine harvester

Positioning accuracy directly affects the operational performance and stability of navigation systems. However, in complex field environments, severe vibrations during combine harvester operation can significantly exacerbate positioning errors. These vibrations have become a key limiting factor for navigation accuracy improvements. This study proposes an adaptive compensation method for navigation positioning errors that considers the vibration characteristics of combine harvesters. The proposed method aims to enhance anti-interference positioning accuracy during fully autonomous harvesting operations. First, a hybrid Whale Optimization Algorithm (WOA)–Light Gradient Boosting Machine (LightGBM) model was developed to identify operational stages. This model was trained using vibration data collected at 100 Hz over 15 s, yielding 1500 time-frame samples. Then, a regression prediction model for positioning errors was established using an eXtreme Gradient Boosting (XGBoost)–Multilayer Perceptron (MLP) framework. This model was built from GNSS/INS error data recorded at 5 Hz over 175 s, resulting in 875 time-frame samples. Finally, GNSS data compensated with the predicted positioning errors were fused with Inertial Navigation System (INS) data using an error-state Kalman filter (ESKF) within the same local planar coordinate system to achieve adaptive error compensation. Experimental results showed high identification accuracy and strong error prediction performance, with a maximum lateral deviation of 0.039 m in the straight-line path tracking test. During autonomous harvesting, the system maintained strong stability with an average lateral deviation of 0.087 m. This study provides a new approach for achieving high-precision positioning of combine harvesters under vibration disturbances. It also offers valuable insights for the development of anti-interference positioning technologies in other ground-based agricultural vehicles. • Effectively reduces the impact of vibration on GNSS positioning accuracy. • An online working-stage identification model is developed for combine harvesters. • An XGBoost-MLP hybrid model is designed for accurate GNSS error prediction. • An adaptive GNSS/INS integration method is established based on real-time error compensation.