对于自动驾驶车辆导航而言,一个耐故障的Sins/双2D-LDV紧密合的集成方案
Zhiyi Xiang1,2, Qi Wang1,2, Shilong Jin1,2
1College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha, 410073, China.
Scientific reports
|October 13, 2025
概括
这项研究介绍了使用带式惯性导航系统 (SINS) 和双2D激光多普勒速度计 (2D-LDVs) 的陆地车辆的容错导航系统. 这种新的方法提高了定位的准确性和可靠性,特别是在GPS被拒绝的地区.
科学领域:
- 机器人技术和自主系统
- 导航和定位技术 导航和定位技术
- 传感器融合和数据集成
背景情况:
- 固定的惯性导航系统 (SINS) 对自主陆地车辆至关重要,但在GNSS被拒绝的环境中性能降低.
- 二维激光多普勒速度计 (2D-LDVs) 提供补充数据,但容易受到车辆侧滑和测量异常值的影响.
- 现有的集成方法与数据异常作斗争,影响导航准确性和系统可靠性.
研究的目的:
- 开发一个耐故障的SINS/双2D-LDV紧密合的集成方案,用于增强自主陆地车辆导航.
- 为了应对车辆侧滑和2D-LDV测量中的异常值所带来的挑战.
- 在具有挑战性的导航场景中提高定位精度和系统稳定性.
主要方法:
- 实施了一种冗余的测量模型,将两个2D-LDV与SINS集成在一起,并结合了车辆的侧向零速度约束.
- 引入了基于局部异常因素 (LOF) 的故障检测方法,以识别测量异常值和约束违规行为.
- 采用了具有LOF调整增益的自适应过器,以减轻检测到的异常对导航解决方案的影响.
主要成果:
- 拟议的SINS/Dual-2D-LDV方案在广泛的车辆实验中,与传统方法相比,显示出更高的水平和垂直定位精度.
- 基于LOF的故障检测方法在识别异常方面表现出更高的灵敏度和有效性,而不是传统的剩余基平方方法.
- 通过真实世界长途车辆试验验证了耐故障集成方案的稳定性和可靠性.
结论:
- 新的耐故障SINS/双2D-LDV集成方案显著提高了自动陆地车辆导航的准确性和可靠性.
- 与现有方法相比,基于LOF的异常检测为处理测量异常值和约束违规提供了更有效的解决方案.
- 这种方法为在没有GNSS的环境中提供了可靠的自主导航的有希望的解决方案.
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