基于深度神经网络的融合本地化使用智能手机
Suqing Yan1,2, Yalan Su2, Jianming Xiao3
1Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
这项研究引入了一种新的深度学习系统,将磁场数据和行人死亡计算 (PDR) 融合在一起,以实现准确的室内定位. 改进的方法提高了复杂环境中的定位可靠性和实用性.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 地理学工程 工程地质学
背景情况:
- 室内基于位置的服务 (LBS) 对智能环境至关重要,磁场定位是一个有希望的无基础设施的方法.
- 磁性定位的挑战包括由于大规模,对称的室内空间中的噪音和信号相似性的模两可的特征.
研究的目的:
- 通过融合磁场数据和行人死亡计算 (PDR) 来开发一个强大而准确的室内定位系统.
- 在复杂的室内环境中克服传统磁性定位方法的局限性.
主要方法:
- 一个ResNet-GRU-LSTM神经网络被提议用于增强磁性定位精度.
- 开发了一种多功能驱动步骤长度估计,使用层次GRU (H-GRU) 模型与加速和陀螺仪数据进行估计.
- 一个颗粒过器框架整合了这些方法,以可靠地定位行人.
主要成果:
- 拟议的融合系统在实验试验中表现出与传统本地化算法相比更高的准确性和稳定性.
- 该系统实现了可靠的实时室内定位,具有较低的计算复杂性和成本.
结论:
- 基于深度神经网络的融合系统有效地集成磁性和PDR数据,以改善室内定位.
- 该方法为无处不在的室内定位需求提供了一个实用,准确和可通用的解决方案.
相关概念视频
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