基于LSTM的GNSS定位使用卫星测量特征与伪色残留物联合使用
Ibrahim Sbeity1,2, Christophe Villien1, Benoît Denis1
1Université Grenoble Alpes, CEA-Leti, F-38000 Grenoble, France.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
机器学习通过预测测量的质量来增强全球导航卫星系统 (GNSS) 的定位. 这种方法优化了卫星数据的选择,在具有挑战性的环境中提高了准确性.
科学领域:
- 地理学工程 工程地质学
- 卫星导航系统 卫星导航系统
- 机器学习应用 机器学习应用
背景情况:
- 全球导航卫星系统 (GNSS) 中卫星数量的增加在选择准确的伪范围数据方面带来了挑战.
- 偏差测量显著影响定位准确性,特别是在单个时代的场景中.
研究的目的:
- 利用机器学习来预测链接式测量质量因素.
- 通过利用预测的质量因子,优化GNSS定位中的测量权重.
主要方法:
- 创建了一个自定义的特征矩阵,包括条件伪范围余量和每个链接的卫星指标 (例如,载体对噪声功率密度比,高度,载体相锁定时间).
- 使用长期短期记忆 (LSTM) 深度神经网络来利用特征相关性和预测测量的权重.
主要成果:
- 拟议的机器学习解决方案与传统方法相比,表现优越.
- 在真实世界数据上的实验结果证实了该方法在提高定位精度方面的有效性.
结论:
- 开发的方法在GNSS测量权重和选择方面取得了重大进展.
- 这种方法对在GNSS挑战的操作条件下增强定位具有特别的希望.
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