面向GNSS多路径LSTM训练模型的可解释的人工智能
He-Sheng Wang1, Dah-Jing Jwo1, Zhi-Hang Gao1
1Department of Communications, Navigation, and Control Engineering, National Taiwan Ocean University, 2 Peining Road, Keelung 202301, Taiwan.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
本研究介绍了一种可解释的深度学习框架,用于全球导航卫星系统 (GNSS) 多路径检测. 层级相关性传播 (LRP) 揭示信号异常,提高导航系统的可靠性.
科学领域:
- 地理学工程 工程地质学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 全球导航卫星系统 (GNSS) 越来越多地使用深度学习来处理信号.
- 深度学习模型中缺乏可解释性对安全关键的GNSS应用构成风险.
- 多路径效应是GNSS信号分析的一个重大挑战.
研究的目的:
- 开发一种可解释的深度学习框架,用于在GNSS中检测多路径效应.
- 提高GNSS应用中使用的深度学习模型的可解释性.
- 建立一种用于GNSS信号中异常检测的新方法.
主要方法:
- 为GNSS可观测物开发了一个可解释的长短期内存 (LSTM) 架构.
- 适应层级相关性传播 (LRP) 用于GNSS信号分析,用于归因模型决策.
- 与信号异常相关的LRP相关性得分用于异常检测.
主要成果:
- 在GNSS参数中,LSTM模型实现了高预测准确性,同时保持了可解释性.
- 在异常信号条件下,LRP相关性得分始终增加 (7.34%-32.48%).
- 观察到的具体增加:多路径参数 (7.34-8.81%),载体与噪声的比率 (12.50-32.48%),升高角度 (16.10%).
结论:
- 基于LRP的分析增强了GNSS信号质量监测和完整性评估.
- 拟议的方法为检测和分析GNSS信号异常提供了实际框架.
- 这项工作有助于开发更可靠和值得信赖的导航系统.
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