基于功能模式与深度网络模型匹配的位置估计
Yu-Ting Bai1,2, Wei Jia1, Xue-Bo Jin1,2
1School of Artificial Intelligence, Beijing Technology and Business University, Beijing, China.
Frontiers in neurorobotics
|June 30, 2023
概括
这项研究引入了一种新的行人定位方法,仅使用惯性测量,克服全球定位系统 (GPS) 信号中断. 这种方法通过将惯性数据特征与深度网络相匹配以实现精确定位来提高准确性.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 地理学就是地质学.
背景情况:
- 全球导航卫星系统 (GNSS) 和全球定位系统 (GPS) 的信号经常在城市峡谷和道等具有挑战性的环境中丢失.
- 在这些信号中断期间准确地定位行人仍然是一个重大的技术挑战.
研究的目的:
- 提出和评估一种新的位置估计方法,仅依赖于行人导航的惯性测量.
- 解决基于GPS的定位在信号阻塞的环境中的局限性.
主要方法:
- 开发了一个深度网络框架,用于从惯性测量中提取特征和匹配.
- 功能提取和分类技术用于模式分区.
- 分析了深度网络模型,并训练了不同的惯性测量模式,以获得本地化信息.
主要成果:
- 拟议的方法通过使用适合不同特征模式的深度网络来提高位置估计的准确性.
- 这种方法有效地提高了在GPS信号中断期间行人定位的准确性.
结论:
- 仅使用惯性测量进行行人定位是可行的,并且可以达到高精度.
- 深度网络模型与功能模式匹配相结合,为在GNSS拒绝环境中的导航提供了强大的解决方案.
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