网络智慧:一个网络物理深度无线室内定位系统和数字双胞胎方法
Muhammed Zahid Karakusak1,2, Hasan Kivrak3, Simon Watson4
1Graduate School of Engineering and Natural Sciences, Istanbul Medipol University, 34810 Istanbul, Turkey.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
这项研究引入了用于无线室内定位的新型网络物理系统,减少了用于Wi-Fi无线电图的手动数据收集. 自主机器人和深度学习实现了准确的本地化,在具有挑战性的环境中提高了安全性和效率.
科学领域:
- 网络物理系统 网络物理系统
- 无线室内定位无线定位
- 机器人技术 机器人技术 机器人技术
背景情况:
- 传统的无线室内定位依赖于接收信号强度 (RSSI) 指纹.
- 现有的方法需要费力的Wi-Fi现场调查来构建无线电地图,这导致了高成本和过时的问题.
- 挑战包括适应性,可扩展性和适用于危险或偏远环境的适应性.
研究的目的:
- 介绍一个新的网络物理无线室内定位系统,解决指纹技术中的数据收集挑战.
- 开发一种可适应,可扩展的系统,用于在大型和不断变化的环境中构建无线电地图.
- 通过准确的地面真相数据收集,降低劳动力成本并提高室内定位的效率.
主要方法:
- 实施一个具有数字双胞胎接口的网络物理系统,以提供知情决策.
- 在实时使用地面机器人进行自主无线电地图构建.
- 应用深度学习模型 (MLP,LSTM模型1,LSTM模型2) 用于基于RSSI的室内定位.
主要成果:
- 该系统成功地在具有挑战性的环境中自主生成了无线电地图.
- 基于RSSI的室内定位使用深度学习模型实现了≤2.16m的平均定位误差.
- LSTM Model 2 展示了卓越的性能,平均误差为 1.55 m 和 1.97 m,超过 81% 的误差在 2 m 内.
结论:
- 拟议的网络物理系统有效地利用动态Wi-Fi RSS调查和深度学习来实现准确的室内定位.
- 这种方法提供了实际的应用性,适合于现实世界的部署,并改善了危险地点的人类安全.
- 这种方法提高了效率,并解决了无线电地图的过时性,其性能与现有文献相美.
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