一种LSTM和因子图的混合算法,用于在GNSS中断期间改善联合GNSS/INS定位精度
Fuchao Liu1,2, Hailin Zhao1,2, Wenjue Chen1,2
1School of Automation, Beijing Information Science & Technology University, Beijing 100192, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
这项研究引入了一种新的方法,使用长短期记忆 (LSTM) 神经网络来预测全球导航卫星系统 (GNSS) 在信号中断期间的数据. 这提高了联合GNSS/惯性导航系统 (INS) 对陆地车辆定位的准确性和稳定性.
科学领域:
- 机器人技术和自主系统
- 地理学工程 工程地质学
- 人工智能的人工智能
背景情况:
- 城市环境导致全球导航卫星系统 (GNSS) 信号中断,降低了陆地车辆联合GNSS/惯性导航系统 (INS) 定位的准确性.
- 在GNSS中断期间保持可靠的定位对于自主导航和先进的驾驶辅助系统至关重要.
研究的目的:
- 开发一种可靠的方法来预测信号中断期间的GNSS信息,使用长期短期记忆 (LSTM) 神经网络.
- 将这种预测能力集成到基于因子图的框架中,以提高GNSS/INS定位准确性和连续性.
主要方法:
- 当GNSS信号可用时,使用因子图融合算法进行数据融合.
- 使用INS速度,IMU数据和GNSS增量定位来训练长期短期记忆 (LSTM) 神经网络模型.
- 在GNSS中断期间,经过训练的LSTM模型预测了GNSS的位置增量,以生成用于组合定位的伪GNSS信息.
主要成果:
- 现实世界的道路测试证明了拟议的LSTM增强的因子图局部化方法的有效性.
- 与传统的GNSS/INS联合方法相比,该方法在频繁信号丢失的环境中提供了更准确和更强大的定位结果.
结论:
- 拟议的基于LSTM的预测方法在GNSS信号中断期间显著提高了GNSS/INS联合定位系统的性能.
- 这种方法提供了一种可靠的解决方案,用于在具有挑战性的城市环境中连续和准确地定位车辆.
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