Optimized IDW Algorithm for Accurate GPS-IMU Time Synchronization Using Acceleration and Temporal Factors
Kemeng Li, Hongli Zhang, Yinggang Wang, Jun Lei · IEEE Sensors Journal · 2025
Accurate time synchronization of GPS-IMU sensor data is critical in dynamic applications such as autonomous driving and UAV navigation, where rapid acceleration variations challenge traditional interpolation methods. This study proposes TDAR-IDW (Time Difference and Acceleration Rate-Inverse Distance Weighting), an adaptive algorithm that integrates time difference and acceleration variation into an exponential weighting framework. Unlike conventional IDW, TDAR-IDW employs dual-factor weights:wti=e-α(Δti)pandwai=e-β(Δai)qto prioritize temporally proximate data and suppress erratic motion effects. These weights are dynamically fused via state-dependent exponents, enabling realtime adaptation under varying motion conditions. Experimental validation on a GPS-IMU platform (1 Hz GPS, 100 Hz IMU) confirms TDAR-IDW’s superiority over linear interpolation, spline interpolation, and traditional IDW. Under rapid acceleration, TDAR-IDW reduces roll angle error by 92.3% and pitch angle error by 75.0%, achieving near-perfect Pearson correlations (ρ > 0.999). For gradual changes, roll and pitch angle errors decrease by 69.0% and 77.1%, while total acceleration field prediction MAE improves by 34.9%. The error distribution (IQR) is reduced by 58%, reinforcing the algorithm’s stability and robustness. By addressing polynomial instability and static weight coupling in existing methods, TDAR-IDW provides a scalable and computationally efficient solution for multi-sensor fusion in dynamic IoT, robotics, and intelligent transportation systems.