一种基于卡尔曼波器 (ESKF) 的先进错误状态地形轮匹配 (TERCOM) 方法,用于使用低成本的数字海拔地图跟踪飞行器
Muhammad Bilal Kadri1, Sofia Yousuf2
1College of Computer & Information Science (CCIS), Prince Sultan University, Riyadh, Saudi Arabia.
PeerJ. Computer science
|September 24, 2025
概括
本研究引入了一种新的模糊启发式方法,用于无人机 (UAV) 的地形辅助导航 (TAN). FH-MAD方法显著减少了计算时间,并提高了实时应用程序的导航精度.
科学领域:
- 机器人技术和自主系统
- 导航和控制系统 导航和控制系统
- 在工程领域的人工智能.
背景情况:
- 地形辅助导航 (TAN) 对于无人机 (UAV) 的准确性至关重要.
- 传统的TAN方法面临的局限性是由于耗时的地图相关性,特别是数字海拔地图 (DEM).
- 计算复杂性阻碍了传统的TAN算法的实时应用.
研究的目的:
- 为无人机 (UAV) 开发一个计算效率高的地形辅助导航 (TAN) 算法.
- 为了减少在TAN系统中的地图相关过程的执行时间和计算复杂性.
- 为了提高TAN对实时无人机导航的准确性和适用性.
主要方法:
- 为平均绝对偏差 (MAD) 相关性方案 (FH-MAD) 开发了一种模糊启发式方法.
- 模糊逻辑利用车载传感器数据 (标题,滚动) 来识别匹配的地图区域.
- 一个错误状态卡尔曼波器 (ESKF) 已集成用于机动期间的位置估计.
主要成果:
- 与传统的TAN技术相比,FH-MAD方法证明了计算时间的显著减少.
- 在使用各种数字海拔地图 (DEM) 的测试中观察到更好的位置精度.
- 拟议的系统在各种地形条件和无人机操纵场景中被证明是有效的.
结论:
- FH-MAD算法为无人机中的TAN提供了一个计算效率高,准确的解决方案.
- 模糊逻辑和ESKF的整合增强了实时导航能力.
- 这种方法非常适合实际,实时的无人机导航应用程序,要求高精度和速度.
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