一个TCN-BiLSTM和ANR-IEKF混合框架,用于在GNSS中断期间持续定位车辆
Senhao Niu1, Jie Li1, Chenjun Hu1
1National Key Laboratory of Dynamic Testing Technology for Extreme Environment Optoelectronics, North University of China, Taiyuan 030051, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
本研究引入了混合深度学习和自适应卡尔曼波器框架,以改善全球导航卫星系统/惯性导航系统 (GNSS/INS) 在信号中断期间的定位. 这种新的方法在具有挑战性的城市环境中提高了50%以上的导航精度.
科学领域:
- 机器人技术和自主系统
- 导航和定位 导航和定位
背景情况:
- 综合全球导航卫星系统和惯性导航系统 (GNSS/INS) 的性能在城市峡谷中因信号阻塞而下降.
- 可靠,连续定位对于自动驾驶应用至关重要.
研究的目的:
- 开发一个强大的混合框架,用于在GNSS被拒绝的环境中高精度导航.
- 在信号中断期间提高GNSS/INS系统的可靠性.
主要方法:
- 这是一个新的框架,它结合了时间卷积网络和双向长期短期内存 (TCN-BiLSTM) 进行伪GNSS生成.
- 适应式噪声调节代扩展卡尔曼波器 (ANR-IEKF) 用于融合伪GNSS和INS数据.
- 验证现实世界的车辆数据集,包括直线和转场景.
主要成果:
- 与基线模型相比,ANR-IEKF + TCN-BiLSTM框架显示出更高的定位准确性和稳定性.
- 在70秒的GNSS中断期间,实现了超过50%的定位精度提升.
- 与强大的深度学习基线相比,定位误差减少到大约3.4米.
结论:
- 拟议的框架提供了一种可靠的解决方案,用于在GNSS被拒绝的环境中保持高精度导航.
- 适应式卡尔曼波器通过动态调整噪声统计数据,有效地提高了稳定性.
- 在信号中断期间,TCN-BiLSTM模型成功生成了准确的伪GNSS测量.
关键词:
双向LSTM (BiLSTM) 是一个双向的LSTM.在GNSS中断.作为GNSS/INS综合导航系统的一体化导航系统.全球导航卫星系统 (GNSS) 是一个全球导航卫星系统.惯性导航系统 (INS) 是指惯性导航系统.代扩展卡尔曼波器 (IEKF) 是一种代扩展卡尔曼波器.时间卷积网络 (TCN)更多相关视频
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