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为提高单一模型卫星钟差预报的精度和稳定度，提出了一种灰色模型与一阶加权局域法组合的卫星钟差预报方法。首先，采用db1小波对卫星钟差进行三层多尺度分解和单支重构，得到一项近似分量和三项细节分量；然后，采用灰色模型对近似分量和一阶加权局域法对细节分量分别进行预报；最后，将各分量的预报结果对应相加即可得到卫星钟差的最终预报值。采用武汉大学GNSS分析中心发布的精密卫星钟差产品，随机选取10颗Galileo卫星的钟差数据进行预报实验。结果表明：在2、4、6 h预报时，该模型相比于灰色模型的预报精度分别提高了50.00%、64.66%、60.48%，稳定度分别提高了18.07%、23.19%、32.51%；在改变建模数据量和预报长度时，该模型的预报精度和稳定度均显著高于其他单一模型，其预报精度和稳定度最大可以提高85.67%和89.28%，验证了该组合模型在卫星钟差预报方面的有效性和可行性。
Abstract：A method for satellite clock bias(SCB)prediction combining the grey model and first-order weighted local method was proposed to improve the accuracy and stability of SCB prediction based on a single model. First, a db1 wavelet was used to perform a three-layer multiscale decomposition and single-branch reconstruction of the SCB, obtaining an approximate component and three detailed components. Subsequently, the grey model was adopted to predict the approximate components, and the first-order weighted local method was used to predict the detailed components, respectively. Finally, the predicted values of all components were added to obtain the final predicted value of the SCB. The clock bias data of ten Galileo satellites were randomly selected for prediction experiments using precision SCB products released by the GNSS Analysis Center of Wuhan University. The results show that in the 2 h, 4 h and 6 h predictions,the prediction accuracy of this combined model increases by 50.00%, 64.66% and 60.48%, respectively, while the stability increases by 18.07%, 23.19% and 32.51%, respectively, compared with those of the grey model. When the amount of modeling data and prediction length were changed, the prediction accuracy and stability of the combined model are significantly improved compared with those of other single models. Its prediction accuracy and stability can be increased by up to 83.12% and 86.28%, respectively. Thus, the effectiveness and feasibility of the combined model for SCB prediction are verified.
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基本信息:
DOI：10.20189/j.cnki.CN/61-1527/E.202506005
中图分类号:P228
引用信息:
[1]于烨,杨朝攀,金国栋,等.联合灰色模型与一阶加权局域法的卫星钟差中短期预报[J].火箭军工程大学学报,2025,39(06):44-54.DOI:10.20189/j.cnki.CN/61-1527/E.202506005.
基金信息:
国家自然科学基金（12403080）; 中国博士后科学基金面上项目（2024M764304）; 国家资助博士后研究人员计划（GZC20233565）; 中国博士后科研业绩评估考核资助项目（YJC20251197）; 陕西省自然科学基础研究计划（2023-JC-QN-0027）