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室内行人位置通过基于滑动窗户的因子图表优化
Yu Cheng1, Haifeng Li2, Xixiang Liu1
1School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究介绍了一种先进的室内行人定位方法,使用智能手机传感器和变压器+TCN模型进行精确的步骤长度估计. 新方法显著提高了在具有挑战性的室内环境中的定位准确性和可靠性.
科学领域:
- 机器人和智能系统 机器人和智能系统
- 传感器融合和导航
- 机器学习用于信号处理.
背景情况:
- 全球导航卫星系统 (GNSS) 由于信号阻塞和多路径效应,在室内无效.
- 配备微电机系统 (MEMS) 传感器的智能手机提供了室内定位的潜力.
- 准确的室内行人定位对于各种应用至关重要.
研究的目的:
- 使用智能手机传感器开发一种高精度和可靠的室内行人死亡计数 (PDR) 方法.
- 为了提高步骤长度估计的准确性和稳定性.
- 为了提高室内整体定位性能.
主要方法:
- 使用惯性测量单元 (IMU) /PDR预集成来减轻惯性导航分歧.
- 开发了一种结合变压器和时间卷积网络 (TCN) 架构的新型步骤长度估计算法.
- 实现了滑窗因子图形优化 (SW-FGO) 使用加速度计,陀螺仪和磁力计数据进行准确的姿势估计.
主要成果:
- 与传统的FGO相比,提出的SW-FGO融合方法提高了29.68%的定位精度.
- 基于变压器+TCN的步骤长度估计算法在口袋模式下将绝对位置误差降低了42.15%,而不是LSTM.
- 变压器+TCN模型实现了1.61%的步骤长度估计误差,精度提高了24.41%.
结论:
- 拟议的基于变压器+TCN和SW-FGO的PDR方法显著提高了室内行人定位的准确性和可靠性.
- 这种新的方法有效地解决了复杂的室内环境中传统方法的局限性.
- 这项技术有望改善室内导航和基于位置的服务.
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