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. We propose 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 kalman filter expanded fusion (KFEF) outperforms the traditional interpolation method radial basis function (RBF) and the state-of-the-art spatiotemporal interpolation and prediction methods spatiotemporal Kriging (STK) and Gaussian process (GP), in terms of both reconstruction accuracy and stability of the temperature field. Specifically, KFEF achieves a 61.54% reduction in root mean square error (RMSE) compared with RBF and reductions of 34.21% and 32.43% relative to STK and GP, respectively. This indicates its 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.

利用卡尔曼时空数据融合进行大气折射率修正下的温度高精度采集

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