一个基于RSSI和AoA的深度学习的蓝牙室内定位系统.
Yongjie Yang1,2, Hao Yang1, Fandi Meng1
1School of Information Science and Technology, Nantong University, Nantong 226019, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
这项研究引入了一种新的深度学习蓝牙系统,用于准确的室内定位. 它通过减少环境干扰引起的错误,显著改善传统方法,达到厘米级准确度.
科学领域:
- 无线通信是一种无线通信.
- 室内定位系统 室内定位系统
- 机器学习是机器学习.
背景情况:
- 传统的接收信号强度指标 (RSSI) 和到达角度 (AoA) 方法在复杂的室内环境中受到多路径效应,信号减弱和噪声的影响,限制了定位准确性.
- 现有的室内定位技术需要进一步改进,以克服环境挑战并实现可靠的性能.
研究的目的:
- 开发一种基于深度学习的强大的蓝牙室内定位系统.
- 为了减轻环境干扰对定位准确性的影响.
- 提高传统和新兴室内定位方法的性能.
主要方法:
- 使用卡尔曼波器 (KF) 来减少到达角 (AoA) 测量的角误差.
- 采用中位过器 (MF) 和移动平均过器 (MAF) 来减轻基于接收信号强度指标 (RSSI) 的距离测量的波动.
- 提出了一个深度学习网络架构,将卷积神经网络 (CNN) 与多头注意力 (MHA) 模型相结合,使用反向传播 (BP) 算法进行训练.
主要成果:
- 拟议的系统实现了0.29米的平均定位误差.
- 与传统的RSSI和AoA定位方法相比,显示了显著的准确性改进.
- 与各种新兴的室内定位技术相比,表现出卓越的定位准确性.
结论:
- 开发的基于深度学习的蓝牙室内定位系统有效克服了传统方法的局限性.
- 集成先进的过技术和CNN-MHA模型提高了复杂的室内环境中的定位精度.
- 这种方法为高精度室内定位提供了一个有希望的解决方案.
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