经济高效的基于RSSI的室内接近定位,用于大型/复杂的博物馆展览空间.
Panos I Philippopoulos1, Kostas N Koutrakis1, Efstathios D Tsafaras1
1Digital Systems Department, University of the Peloponnese, GR-23100 Sparta, Greece.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
这项研究测试了蓝牙低能耗 (BLE) 和接收信号强度指示 (RSSI) 在博物馆的室内定位. 简单的方法实现了81.53%的准确率,而机器学习则将其提高到87.24%.
科学领域:
- 室内本地化 室内本地化
- 无线传感器网络是无线传感器网络.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 接收信号强度指示 (RSSI) 提供了简单,具有成本效益的室内定位,但由于非视线 (NLOS) 条件,噪音和多路径色而难以准确.
- 蓝牙低能耗 (BLE) 技术为室内定位提供了可扩展,节能和市场可用的解决方案,特别适合大型复杂环境,如有高访客流量的博物馆.
研究的目的:
- 在现实博物馆环境中评估基于BLE/RSSI的室内定位系统的准确性和可行性.
- 开发和测试一个原型工具 (VTT),集成各种数据处理和预测方法来定位访客细胞水平.
- 将传统信号处理技术的性能与机器学习算法进行比较,以提高本地化准确度.
主要方法:
- 在现代希腊文化博物馆进行了一项试点研究,使用可穿戴的BLE信标来追踪47个大厅的游客.
- 该VTT原型包含了Kalman过器用于RSI平滑,混合定位,时间/空间过和机器学习分类器.
- 模拟了访客的运动,选择了""行为模型进行实验,并在20个RSSI数据集中评估了15种方法/算法.
主要成果:
- 简单的数据平滑和管理方法在各种数据集中实现了平均81.53%的预测准确性.
- 机器学习算法,特别是随机森林,表现出卓越的性能,平均预测准确率为87.24%.
- 实施的基础设施实现了每平方米8欧元的成本效益.
结论:
- 基于BLE/RSSI的本地化是博物馆等大型公共空间的室内定位的可行和经济有效的解决方案.
- 与传统的信号处理方法相比,机器学习算法显著提高了本地化准确性.
- VTT原型提供了一个可扩展和可适应的架构,用于改善复杂环境中的室内本地化.
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