摘要
利用主成分分析法(PCA)确定了观测噪声中高度角和信噪比(SNR)的贡献,在此基础上建立了精化的全球卫星导航系统(GNSS)随机模型,并验证了基于此随机模型的高纬度测站精密单点定位(PPP)效果.结果表明:在高纬度地区精化随机模型相比于仅顾及高度角或SNR的传统随机模型效果更佳,定位精度较高度角模型提高约30%,较SNR模型提高约20%.在高程方向上精度提高最为明显,对比高度角模型和SNR模型分别提高约37%和24%.该研究对提高高纬度地区GNSS定位精度有一定的参考价值.
Principal component analysis method is used to determine the contribution of elevation angle and signal to noise ratio(SNR)in observation noise,and a refined Global Navigation Satellite System(GNSS)stochastic model is established based on the analysis results.The performance of the refined stochastic model is verified by using precision point positioning(PPP).It shows that the refined stochastic model leads to better positioning results in high latitude areas than traditional model that only takes into account elevation angle or SNR.The refined stochastic model is about 30%more accurate than elevation angle model,and about 20%better than SNR model.The accuracy of refined stochastic model improves most obvious in the zenith direction,and the improvements are about 38%and 24%with respect to the results of elevation angle model and SNR model,respectively.This study indicates that our new refined stochastic model is advantage to high-precision positioning accuracy in high latitude areas.
作者
吕明慧
李薇
张宝成
柴艳菊
LYU Minghui;LI Wei;ZHANG Baocheng;CHAI Yanju(Innovation Academy for Precision Measurement Science and Technology,CAS,Wuhan 430071,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《全球定位系统》
CSCD
2021年第3期15-23,53,共10页
Gnss World of China
基金
国家自然科学基金(42074037,41674020)。
