伪距
全球导航卫星系统应用
计算机科学
加权
背景(考古学)
卫星
卫星系统
卫星导航
全球定位系统
实时计算
数据挖掘
人工智能
遥感
工程类
电信
医学
生物
放射科
地质学
航空航天工程
古生物学
作者
Ibrahim Sbeity,Christophe Villien,Benoît Denis,Elena Veronica Belmega
标识
DOI:10.1109/icl-gnss57829.2023.10148922
摘要
In the Global Navigation Satellite System (GNSS) context, the growing number of available satellites has lead to many challenges when it comes to choosing the most accurate pseudorange contributions, given the strong impact of biased measurements on positioning accuracy, particularly in single-epoch scenarios. This work leverages the potential of machine learning in predicting link-wise measurement quality factors and, hence, optimize measurement weighting. For this purpose, we use a customized matrix composed of heterogeneous features such as conditional pseudorange residuals and per-link satellite metrics (e.g., carrier-to-noise power density ratio and its empirical statistics, satellite elevation, carrier phase lock time). This matrix is then fed as an input to a recurrent neural network (RNN) (i.e., a long-short term memory (LSTM) network). Our experimental results on real data, obtained from extensive field measurements, demonstrate the high potential of our proposed solution being able to outperform traditional measurements weighting and selection strategies from state-of-the-art.
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