Satellite clock bias(SCB)prediction is essential for enhancing the accuracy and reliability of real-time precise point positioning(RT-PPP)in Global Navigation Satellite Systems(GNSS).To address the nonlinearity,non-st...Satellite clock bias(SCB)prediction is essential for enhancing the accuracy and reliability of real-time precise point positioning(RT-PPP)in Global Navigation Satellite Systems(GNSS).To address the nonlinearity,non-stationarity,and short-term interruptions of SCB data under complex environments,this paper proposes an enhanced SCB prediction model combining Temporal Convolutional Networks(TCN)and Transformers.Experimental results indicate that,in a 24-h prediction task,the proposed model reduces root mean square error(RMSE)and range error(RE)by 95.6%,86.0%,and 61.3%,and93.7%,86.3%,and 58.8%,respectively,compared with LSTM,Transformer,and CNN-BiGRU-Attention models,while improving computational efficiency by 48.6%over the Transformer.Moreover,although the clock bias products generated by the proposed method result in slightly higher static PPP positioning errors than the International GNSS Service(IGS)rapid clock products,the error differences are generally at the millimeter level,demonstrating the feasibility of using predicted clock bias products to replace rapid clock products in the short term.This method addresses the PPP positioning issue during short-term network service interruptions from the perspective of time series prediction and provides potential solutions for engineering applications such as landslide,earthquake,and subsidence monitoring.展开更多
无电离层组合模型和非差非组合模型是精密单点定位(precise point positioning,PPP)中最常用的两种函数模型。非差非组合模型中电离层误差常被描述为随机游走,随机游走过程中的功率谱密度成为决定PPP定位性能的主要因素,采用经验值功率...无电离层组合模型和非差非组合模型是精密单点定位(precise point positioning,PPP)中最常用的两种函数模型。非差非组合模型中电离层误差常被描述为随机游走,随机游走过程中的功率谱密度成为决定PPP定位性能的主要因素,采用经验值功率谱密度的方法没有考虑电离层小尺度变化。在非差非组合模型的基础上,分析电离层时间相关性信息,在电离层差分时间间隔较小时,观测噪声较大甚至淹没电离层的变化。因此,通过平滑去噪的方法削弱观测值噪声的影响,实时确定电离层功率谱密度,对非差非组合模型中的电离层延迟参数进行合理约束,从而改善定位性能。对12个测站10 d的北斗卫星导航系统(BeiDou satellite navigation system,BDS)数据进行不同电离层模型下的解算,结果表明:相对于传统无电离层组合PPP模型,所提方法在收敛时间上缩短约8.2%,水平方向精度相当,垂直方向定位精度提高约31%。相较于功率谱密度采用经验值方法,所提方法在收敛时间上缩短约9.7%,水平方向精度相当,垂直方向定位精度提高约31%。展开更多
基金supported by the National Natural Science Foundation of China(42304050)Major Science and Technology Projects in Anhui Province,grant number(202103a05020026)+1 种基金Open Foundation of the Key Laboratory of Universities in Anhui Province for Prevention of Mine Geological Disasters(2022-MGDP-08)University Natural Science Research Project of Anhui Province(2023AH051190)。
文摘Satellite clock bias(SCB)prediction is essential for enhancing the accuracy and reliability of real-time precise point positioning(RT-PPP)in Global Navigation Satellite Systems(GNSS).To address the nonlinearity,non-stationarity,and short-term interruptions of SCB data under complex environments,this paper proposes an enhanced SCB prediction model combining Temporal Convolutional Networks(TCN)and Transformers.Experimental results indicate that,in a 24-h prediction task,the proposed model reduces root mean square error(RMSE)and range error(RE)by 95.6%,86.0%,and 61.3%,and93.7%,86.3%,and 58.8%,respectively,compared with LSTM,Transformer,and CNN-BiGRU-Attention models,while improving computational efficiency by 48.6%over the Transformer.Moreover,although the clock bias products generated by the proposed method result in slightly higher static PPP positioning errors than the International GNSS Service(IGS)rapid clock products,the error differences are generally at the millimeter level,demonstrating the feasibility of using predicted clock bias products to replace rapid clock products in the short term.This method addresses the PPP positioning issue during short-term network service interruptions from the perspective of time series prediction and provides potential solutions for engineering applications such as landslide,earthquake,and subsidence monitoring.
