应用IMU/GPS集成导航系统基于自适应无气味卡尔曼过算法在森林救援人员的3D定位
Shengli Pang1, Bohan Zhang1, Jintian Lu1
1College of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
准确的定位对于救援行动至关重要. 本研究介绍了一种适应性无气味卡尔曼波器 (AUKF),用于使用GPS和IMU进行增强的导航,在森林环境和信号丢失时优于其他波器.
科学领域:
- 机器人技术和自主系统
- 地理学工程 工程地质学
- 传感器融合式传感器
背景情况:
- 可靠的定位对于紧急救援人员和行动至关重要.
- 像GPS这样的全球导航卫星系统 (GNSS) 在充满挑战的环境中 (如茂密的森林) 遭受信号不稳定.
- 集成多个传感器,包括惯性测量单元 (IMU) 和GPS,对于稳健而准确的定位至关重要.
研究的目的:
- 开发和评估一个先进的综合导航系统,用于在复杂的环境中精确的3D定位救援人员.
- 提高定位系统的稳定性和准确性,特别是在全球导航卫星系统 (GNSS) 信号中断期间.
- 在现实的救援场景中,将拟议的自适应无气味卡尔曼过器 (AUKF) 与传统过方法的性能进行比较.
主要方法:
- 实现一个具有自适应测量噪声方差矩阵的自适应无气味卡尔曼波器 (AUKF) 算法.
- 整合来自全球定位系统 (GPS),惯性测量单位 (IMU) 和气压高度计的传感器融合数据.
- 通过2D和3D森林道路场景的实地测试来评估性能,包括崎的地形和模拟的GPS信号中断.
主要成果:
- 基于AUKF的集成导航系统与扩展卡尔曼波器 (EKF),无气味卡尔曼波器 (UKF) 和自适应扩展卡尔曼波器 (AEKF) 相比,显示出更高的定位精度.
- 观察到显著的误差减少:18.32%在北方,8.51%在上方,和3.85%在东方方向相比,EKF在崎的森林道路上.
- 该系统表现出极好的适应性,并在GPS信号中断时保持连续,准确的定位.
结论:
- 基于AUKF的集成导航系统为救援人员在具有挑战性的森林环境中定位提供了更高的稳定性和准确性.
- 适应性过技术对于克服GNSS信号限制和确保在紧急操作期间可靠的导航至关重要.
- 拟议的系统为提高搜救任务的安全性和效率提供了可行的解决方案.
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