连接飞机机的深度学习室内定位:一种具有现实世界验证的ResNet方法
Paul Schwarzbach1, Muhammad Ammad1, Michael Schultz2
1Transport Systems Information Technology, TUD Dresden University of Technology, 01062 Dresden, Germany.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
本研究引入了使用残余神经网络 (ResNet) 进行深度学习的方法,用于在飞机机中准确的室内定位. 与传统技术相比,ResNet方法显著提高了本地化准确性.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 航空航天工程 航空航天工程
背景情况:
- 飞机机的室内定位面临着诸如多路径传播和由于金属机身造成的视线不清条件等挑战.
- 现有的基于无线电的定位方法因这些具有挑战性的客环境而退化.
研究的目的:
- 验证深度学习方法,特别是残余神经网络 (ResNet),用于准确地定位飞机机.
- 评估基于ResNet的方法与已建立的定位算法对比的性能.
主要方法:
- 使用的双技术范围从超宽带 (UWB) 和蓝牙低能耗 (BLE).
- 将范围观测转换为由ResNet处理的空间概率表示.
- 在A320机模型中进行了现实世界的测量,其中有19个分布式位置.
主要成果:
- ResNet实现了0.177m的中位定位误差,超过了代最小方程,强大的最小方程和贝叶斯格子过.
- 证明了基于概率的神经网络定位在飞机机应用中的可行性.
- 确定了对可见度,测量高度和传播条件的性能依赖.
结论:
- 深度学习,特别是ResNet,为在具有挑战性的飞机机环境中精确的室内定位提供了可行的解决方案.
- 与基线技术相比,开发的方法显示出更高的准确性.
- 进一步的研究应该考虑环境因素,以实现最佳部署.
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