【摘要】 在绝对距离测量与定位应用中，大气折射误差是制约测量精度的关键因素，其中温度参数对大气折射率的计算具有主导作用。然而，在野外复杂、动态的环境中，由于传感器布设数量有限且环境具有非平稳性，沿测距路径精准获取温度场仍存在较大难度。针对上述问题，本文提出一种面向大气折射修正的温度时空数据融合方法，在卡尔曼滤波框架下融合广义回归神经网络（GRNN）与克里金插值的优势，实现温度参数的动态预测与高精度重构。通过仿真分析以及室内和公里级室外实测实验对所提方法进行了系统验证。仿真结果表明，卡尔曼滤波扩展融合方法（KFEF）在温度场重构精度与稳定性方面均优于传统插值方法径向基神经网络（RBF）和先进的时空插值预测方法时空克里金（STK）和高斯过程回归（GP）：相较于RBF，均方根误差（RMSE）降低了61.54%；相较于STK和GP,RMSE分别降低了34.21%和32.43%。这表明该方法在长距离高精度测距工程应用中具有良好的实用价值。此外，所提出的时空数据融合框架具有高度通用性和可扩展性，同样适用于其他温度场预测与重建问题。更多还原

【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.更多还原