基于深度学习的无MEMS-INS/地磁导航系统 强大的跟踪 平方根立方形卡尔曼波器
Tianshang Zhao1, Chenguang Wang1, Chong Shen1
1The State Key Laboratory of Dynamic Measurement Technology, and The School of Instrument and Electronics, North University of China, Taiyuan 030051, China.
Micromachines
|October 28, 2023
概括
本研究介绍了微电子机械系统-惯性导航系统/地磁导航系统 (MEMS-INS/MNS) 的混合战略,以提高导航准确性. 这种新的深度自学方法提高了定位准确性,即使没有磁场数据.
科学领域:
- 导航系统工程 导航系统工程
- 信号处理 信号处理
- 在导航中使用人工智能
背景情况:
- 微电子机械系统-惯性导航系统 (MEMS-INS) 遭受漂移,限制其导航准确度.
- 地磁导航系统 (MNS) 可以帮助INS,但在地磁解锁的环境中不可靠.
- 在依赖MEMS-INS/MNS的应用中,无和强大的导航至关重要.
研究的目的:
- 提出一种混合无MEMS-INS/MNS战略,以抑制漂移并改善导航连续性.
- 提高集成导航系统的稳定性和计算效率.
- 为了实现高精度的导航估计,即使在MNS信号丢失期间.
主要方法:
- 开发了一种混合无MEMS-INS/MNS策略,将强度跟踪的方根立方卡尔曼波器 (STSRCKF) 与深度自学 (DSL) 结合起来.
- 该方法建立了深度卡尔曼波器增益和最佳估计之间的关系,结合了强跟踪和平方根过与单数值分解.
- 一个非线性自回归神经网络 (NARX) 引入了外源输入,用于深度自我学习能力.
主要成果:
- ST-SRCKF方法实现了1.29°的航向精度误差,比单个INS提高了90.10%,比传统集成导航提高了9.20%.
- 即使在MNS锁定期间,DSL-STSRCKF方法也保持了1.33°的定位准确度,比单个INS有89.80%的改进.
- 拟议的战略证明了持续高精度导航估计的强度和计算效率的提高.
结论:
- 该DSL-STSRCKF战略有效地抑制MEMS-INS漂移,并增强在充满挑战的环境中无导航能力.
- 深度自我学习的整合显著提高了定位准确性和稳定性,特别是在MNS信号中断期间.
- 这种混合方法为MEMS-INS/MNS应用中连续,高精度的导航估计提供了有希望的解决方案.
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