BDS-3 clock error short-term prediction algorithm based on semi-paramet ric-varying-coefficient-SVM combined model
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摘要: 针对卫星钟差序列中非线性特性较为复杂的问题,为了有效地分离周期项改正误差和顾及不能函数化的因素,提高钟差预报的精度,将钟差周期项模型扩充到半参数钟差模型。利用核估计方法,将核函数的窗宽参数与参数解算综合考虑,建立了半参数变系数模型,综合Score检验统计量和支持向量机,进行卫星钟差数据的参数解算、周期项改正分离、异常值识别和残差拟合。首先,引入核估计方法,利用泰勒展式将非参数分量进行修正,综合核函数和窗宽参数,利用三步估计方法得到半参数变系数模型的参数和周期项改正的估计值;然后,构造Score检验统计量进行异常值进行识别,提出了一种综合Score检验统计量的顾及周期项改正的半参数变系数钟差预报模型的异常值识别方法;最后,为了避免对观测值过拟合或拟合不足,对经过预处理的钟差残差数据,利用支持向量机进行进一步拟合,提高模型的拟合和预报精度,采用BDS-3卫星的钟差数据与常用方法进行了对比实验,验证了新方法的可靠性。实验结果表明,本文方法能够精确高效地对BDS-3钟差异常值进行定位,识别并分离周期项改正,有效地提高BDS钟差数据预处理的质量和效率。本文的组合预报模型预报精度优于传统的二次多项式模型、周期项和半参数模型,对于1h、6h和12h预报,本文模型预报BDS-3卫星钟差数据的平均精度优于0.1635 ns。Abstract: The nonlinear characteristics of satellite clock offset sequence are complex, in order to ef fectively separate the correction error of periodic term and take into account the factors that cannot be functionalized, and improve the accuracy of clock bias prediction, the clock error periodic term model is extended to the semi-parametric clock error model. By using kernel estimation method, the window width parameter and parameter solution of kernel function are comprehensively considered, a semi-parametric varying coefficient model is established. Firstly, the kernel estimation method is i ntroduced, the parameter components are modified by Taylor expansion, by synthesizing kernel funct ion and window width parameters, the parameters and periodic term correction estimates of the semi -parametric variable coefficient model are obtained by three-step estimation method; Then, score test statistics are constructed to identify outliers, and a method of identifying outliers is proposed for t he semi-parametric variable coefficient clock difference prediction model with periodic correction; Fi nally, in order to avoid over-fitting or under-fitting of the observed values, support vector machine i s used to further fit the pre-processed clock residual data to improve the fitting and prediction accu racy of the model. The clock difference data of BDS-3 satellite is compared with the conventional methods to verify the reliability of the new method. Experimental results show that the proposed m ethod can accurately and efficiently locate the constant value of BDS-3 clock difference, identify an d separate periodic item corrections, and greatly improve the quality and efficiency of BDS clock d ifference data preprocessing. The prediction accuracy of the combined forecast model in this paper i s better than that of the traditional quadratic polynomial model, periodic term model and semi-para metric model. For 1h, 6h and 12h forecast, the average accuracy of the forecast of BDS-3 satellite clock difference data is better than 0.1635ns.
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