概括
我们开发了一个极化稳定光纤陀螺仪 (IFOG),使用混合机器学习循环 (MLL) 来提高稳定性. 这种先进的IFOG实现了优越的偏移不稳定性,接近萨格纳克干扰度极限,用于强大的惯性导航.
科学领域:
- 光子学和光学工程的工程.
- 惯性导航系统 惯性导航系统
- 机器学习应用 机器学习应用
背景情况:
- 光纤陀螺仪 (IFOG) 对于惯性导航至关重要,但受到极化诱导的漂移的影响.
- 在IFOG中保持规模因子稳定性和高灵敏度仍然是一个挑战.
研究的目的:
- 介绍一款具有极化自我补偿的新型激光驱动IFOG.
- 为了提高规模因素的稳定性,灵敏性和长期的运营稳定性.
- 为了满足实时和强大的惯性导航的严格要求.
主要方法:
- 使用 200kHz 线宽的连贯光,以获得稳定的尺度因子.
- 实施了一种光学方案,确保两极化互惠性和最佳工作点.
- 开发了一种混合机器学习循环 (MLL),将PID和ANN结合起来,通过液晶旋转器 (LCR) 来进行动态极化漂移补偿.
主要成果:
- 实现了高尺度因子稳定性和灵敏性.
- 在MLL方法显著优化偏差不稳定性 (BI) 从0.6723°/h (PID) 到0.3869°/h (MLL) 在200s.
- 在开放环境中,性能接近萨格纳克干扰度极限 (SIL).
结论:
- 开发的IFOG表现出卓越的长期稳定性和准确性.
- 混合MLL有效地弥补了极化合漂移.
- 这种IFOG技术适用于高要求的实时惯性导航应用.
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