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The Superiority of Multi-GNSS L5/E5a/B2a Frequency Signals in Smartphones: Stochastic Modeling, Ambiguity Resolution, and RTK Positioning

  • Weikai Miao
  • , Bofeng Li*
  • , Yang Gao
  • *此作品的通讯作者
  • Tongji University
  • University of Calgary

科研成果: 期刊稿件文章同行评审

摘要

The emerging Internet of Things (IoT) applications, such as intelligent transportation based on vehicular-lane accurate positioning, have a growing demand for precise and reliable positioning with global navigation satellite systems (GNSSs). It is desirable to use GNSS modules in smartphones to achieve high-precision positioning. The GNSS modules in some brands of smartphones thus far are able to track the new L5 signals of GPS and QZSS, E5a signals of Galileo, and B2a signals of BeiDou-3. The L5/E5a/B2a signals have higher quality due to their signal structure, which provides an important potential for high-precision positioning in smartphones. In this article, we will study the quality of L5/E5a/B2a signals, and their superiorities in integer ambiguity resolution (IAR) and precise positioning with respect to the L1/E1/B1 signals from GPS, QZSS, Galileo, and Beidou-2/3 satellites. The signal quality is evaluated in terms of observation precision, multipath, double-differenced ambiguity fractions, and ambiguity dilution of precision (ADOP). In addition, we propose a new weighting model that takes into account the variation range of carrier-to-noise density ratio (C / N textsubscript 0). The results indicate that the Beidou-3 B2a signal has comparable quality to L5/E5a signals of other systems, and all of them are better than the L1/E1/B1 signals. However, the ambiguity fractions of B2a signals diverge abruptly in some periods, resulting in the unsuccessful ambiguity fixing. The L5/E5a/B2a signals can generally obtain higher IAR fix-rate and positioning accuracies than the L1/E1/B1 signals. The new weighting model can capture the smartphone noise characteristics better than the traditional weighting model, thus improving the positioning accuracy.

源语言英语
页(从-至)7315-7326
页数12
期刊IEEE Internet of Things Journal
10
8
DOI
出版状态已出版 - 15 4月 2023
已对外发布

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