机器学习模型用于室内定位使用蓝牙RSSI和视频数据:一个案例研究
Tomás Mamede1, Nuno Silva1, Eduardo R B Marques1,2
1Department of Computer Science, Faculty of Sciences, University of Porto, Rua do Campo Alegre 1055, 4169-007 Porto, Portugal.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究介绍了一种多式联动室内定位系统 (IPS),该系统结合了蓝牙信号和视频. 整体机器学习在具有挑战性的博物馆环境中显著提高了定位准确性.
科学领域:
- 计算机科学 计算机科学
- 机器人技术 机器人技术 机器人技术
- 电气工程 电气工程
背景情况:
- 准确的室内定位对于许多应用来说至关重要.
- 挑战包括信号干扰和环境变化.
- 现有的系统在复杂的环境中往往难以准确.
研究的目的:
- 开发和评估一个多式联网室内定位系统 (IPS).
- 集成蓝牙接收信号强度指示器 (RSSI) 和视频数据.
- 通过机器学习和组合技术来提高IPS的准确性.
主要方法:
- 使用蓝牙RSSI和视频图像实现了一个多式联通IPS.
- 为每个数据源训练独立的机器学习模型.
- 应用集体学习结合模型预测.
- 在博物馆环境中部署和测试了系统,但部署受到了限制.
主要成果:
- 整体模型显著优于单个基于RSSI和基于视频的模型.
- 多式联运数据集成提高了定位准确度.
- 尽管存在多路径干扰,低照明和有限的信标基础设施,该系统仍表现出有效性.
结论:
- 多模式数据融合与集体学习提高了IPS在复杂的室内环境中的准确性.
- 拟议的系统为现实世界部署提供了一个强大的解决方案,具有实际约束.
- 这种方法显示出未来室内本地化应用的巨大潜力.
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