Abstract: In absolute distance measurement and positioning applications, atmospheric refraction error is a critical factor limiting measurement accuracy. Temperature plays a dominant role in computing the atmospheric refractive index. However, accurately acquiring the temperature field along the ranging path in complex and dynamic outdoor environments remains challenging due to limited sensor deployment and environmental nonstationarity. This paper proposes a spatiotemporal temperature data fusion method for atmospheric refraction correction,which integrates the strengths of the generalized regression neural network (GRNN) and kriging interpolation within a Kalman filter. This method achieves dynamic prediction and high-accuracy reconstruction of temperature parameters. The proposed method is systematically validated through simulation analysis as well as indoor and kilometer-scale outdoor experimental measurements. The simulation results demonstrate that KFEF outperforms the traditional interpolation method RBF as well as the advanced spatiotemporal interpolation and prediction methods STK and GP in terms of both reconstruction accuracy and stability of the temperature field. Specifically, KFEF achieves a 61.54% reduction in RMSE compared with RBF and reductions of 34.21% and 32.43% relative to STK and GP, respectively. This indicates its strong practical value for long-distance high-precision ranging engineering applications. Furthermore, the proposed spatiotemporal data fusion framework is highly general and scalable. It can also be applied to other temperature field prediction and reconstruction problems.

FTHOE：一种面向晶圆级互连网络的哈密顿驱动容错路由算法

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