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
文摘北斗三号卫星导航系统(BeiDou-3 Navigation Satellite System,BDS-3)于2020年7月正式开通全球导航定位服务.目前,BDS-3在B1I、B3I、B1C、B2a和B2b频点提供公开服务.伪距单点定位(single point positioning,SPP)因其成本低、算法简单等特点,广泛应用于车辆导航与智能终端定位等领域.为系统评估BDS-3各频点单频SPP性能,并探究北斗全球电离层延迟修正模型(BDS global ionospheric model,BDGIM)的性能,本文构建了适用于BDS-3全频点的单频SPP模型,基于22个全球均匀分布测站的观测数据,对比分析了BDGIM与GPS Klobuchar模型(GPSK8)在太阳活动活跃期与平静期对定位精度的影响.实验结果表明,在全球尺度上,BDGIM性能总体优于GPSK8,在平静期和活跃期的定位精度提升分别为15.6%和11.5%,且在高、低纬度地区改善尤为显著.各频点的单频SPP结果显示,B1C和B1I定位性能最优,3D方向精度分别为3.31 m和3.39 m.
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.
