In order to get clock products with high accuracy in real time,the rapid clock products were used to establish a short-term prediction model.First,the data batch was detrended by fitting and removing with polynomial.Then,the spectrum of residual after detrending was calculated with Fourier transformation.Thus,the modeling period and forecasting period can be confirmed with the periodic characteristics.After this,the RBF (Radial Basis Function)neural network was used to fit and forecast the clock errors.Since the RBF neural network was fit for nonlinear data modeling,this method can get better forecasting results after extracting linear trend and determining reasonable modeling data size.In fact,prediction results indicate that clock error products obtained by the proposed method are shown to have higher accuracy than the ultra-rapid products,and can satisfy decimeter accuracy positioning applications.%为了获得实时高精度GPS钟差,提出了采用快速星历建模进行短期预报.文章先对钟差数据提取趋势项,再利用傅里叶分析研究其周期特征以确定建模与预报时间段长度,最后利用径向基函数(Radial Basis Function,RBF)神经网络建模实时预报钟差.由于RBF神经网络用于非线性数据建模效果良好,在提取线性趋势项并合理确定建模周期后,该方法能够得到较好的预报结果.实际预报结果表明,文中方法得到的预报钟差精度高于超快速星历,能够满足分米级实时精密定位的要求.
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