Relative Localization Error Compensation Under Attitude Disturbances Based on Long Short-Term Memory Residual Learning and Adaptive Extended Kalman Filtering

Abstract

Tracked vehicles operating in hilly and mountainous agricultural environments are frequently subjected to pitch, roll, and vibration, which can introduce time-varying errors into ultra-wideband phase-difference-of-arrival (UWB-PDOA) relative localization. Aiming to improve localization accuracy under such disturbances, this study proposes a relative localization error compensation method that integrates long short-term memory (LSTM) residual learning with a residual-adaptive extended Kalman filter (RAEKF), referred to as LSTM-RAEKF. The proposed method combines UWB-PDOA measurements with inertial measurement unit information to learn disturbance-related localization residuals and adaptively compensate for relative position and theta observations before filtering. A UWB/IMU relative localization test bench was developed, and experiments were performed under static, pitch, roll, and vibration conditions. Across different fixed-point tests, the proposed method reduced the planar position RMSE and theta RMSE by 35.0–62.9% and 54.7–70.8%, respectively. Considering all experimental conditions, the position RMSE decreased from 4.00 cm to 1.81 cm, while the theta RMSE decreased from 5.64° to 2.17°, corresponding to reductions of 54.8% and 61.5%, respectively. Furthermore, LSTM-RAEKF outperformed the standard extended Kalman filter and the innovation-based adaptive estimation extended Kalman filter. Overall, these results demonstrate that LSTM-RAEKF can effectively suppress localization errors induced by attitude disturbances and provide stable relative localization information for subsequent tracked vehicle following control.