基于注意力的LSTM框架,用于在NLOS环境中强大的UWB和INS集成
Meilin Ren1, Junyu Wei2, Jiangyi Qin3
1CATARC Automotive Test Center (Tianjin) Co., Ltd., Tianjin, 300300, China.
Scientific reports
|July 2, 2025
概括
本研究介绍了基于注意力的长短期记忆 (LSTM) 网络,用于超宽带/惯性导航系统 (UWB/INS) 集成. 新的框架通过生成可靠的伪测量来提高非视线 (NLOS) 条件下的定位准确性.
科学领域:
- 导航系统工程 导航系统工程
- 机器人技术中的人工智能
- 信号处理 信号处理
背景情况:
- 超宽带 (UWB) 和惯性导航系统 (INS) 的整合对于精确的定位至关重要.
- 非视线 (NLOS) 传播显著降低了UWB信号质量,影响了定位准确性.
- 现有的基于卡尔曼波器的集成方法在NLOS环境中与UWB信号退化作斗争.
研究的目的:
- 提出一种新的UWB/INS集成框架,使用基于注意力的LSTM神经网络.
- 为了应对在NLOS传播中UWB信号退化的挑战.
- 为了提高UWB/INS本地化在具有挑战性的环境中的稳定性和准确性.
主要方法:
- 基于注意力的长期短期记忆 (LSTM) 神经网络的开发.
- 使用LSTM网络在NLOS条件下生成卡尔曼波器更新的伪测量.
- 实施基于模型和基于学习的技术的混合融合,以实现UWB/INS集成.
主要成果:
- 注意LSTM模型有效地产生伪UWB观测,并保持卡尔曼波器更新.
- 在NLOS场景下,在松散和紧密合的UWB/INS配置中显示的定位错误显著减少.
- 增强时间特征提取和提高伪观测生成的准确性.
结论:
- 拟议的attention-LSTM框架为NLOS条件下UWB/INS本地化提供了一个强大的解决方案.
- 这种混合方法有效地减轻了UWB信号退化挑战.
- 该方法通过先进的深度学习集成来确保精确可靠的本地化.
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