一个精确和可扩展的室内定位系统,使用跨模式的知识蒸
Hamada Rizk1,2, Ahmed Elmogy3,4, Mohamed Rihan5
1Computers & Control Engineering Department, Tanta University, Tanta 31527, Egypt.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
DistilLoc通过将知识从往返时间 (RTT) 传输到收到信号强度指标 (RSSI) 模型来增强室内定位. 这实现了次米准确度,改进了智能环境的现有方法.
科学领域:
- 室内定位和定位系统的定位和定位系统.
- 机器学习用于信号处理.
- 人与计算机的交互和无处不在的计算.
背景情况:
- 准确的用户本地化对于以人为中心的应用程序和可持续发展目标 (SDGs) 至关重要.
- 由于信号限制,GPS在室内无效,而接收信号强度指示器 (RSSI) 方法遭受环境干扰.
- 往返时间 (RTT) 提供了更好的准确性,但缺乏通用接入点支持.
研究的目的:
- 推出DistilLoc,一个新的跨知识蒸框架,用于准确和强大的室内定位.
- 增强基于接收信号强度指标 (RSSI) 的本地化,使用来自往返时间 (RTT) 模型的知识.
- 开发用于室内定位的可扩展和计算高效的解决方案.
主要方法:
- 实现了一个老师-学生架构,其中一个RTT模型 (老师) 训练一个RSSI模型 (学生).
- 利用 FNet 架构来有效处理多个接入点 RSSI 信号.
- 在使用Android设备和谷歌WiFi接入点的各种室内环境中评估DistilLoc.
主要成果:
- 在不同的环境中,在0.42m和0.32m的中位误差下,实现了次米定位精度.
- 显著改善:比传统的RSSI提高267%,比多边化提高496%.
- 验证了DistilLoc的可扩展性和准确性,在部署过程中不需要RTT数据.
结论:
- DistilLoc通过有效地将RTT模型知识转移到RSI模型,提供了一个可扩展和准确的室内本地化解决方案.
- 该框架克服了传统RSSI和RTT方法的局限性,使室内定位更可靠.
- 这项技术支持智能,资源高效的城市环境,为SDG 9和SDG 11做出贡献.
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