基于RSS的室内定位采用空间和频率指纹的双模式CNN方法
Xiangchen Lai1, Yunzhi Luo2, Yong Jia1
1School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究通过将空间和频率数据结合起来,使用接收信号强度 (RSS) 来增强室内定位. 这种新方法显著提高了准确性,降低了超过47%的定位错误.
科学领域:
- 室内定位系统 室内定位系统
- 无线传感器网络是无线传感器网络.
- 信号处理 信号处理
背景情况:
- 基于接收信号强度 (RSS) 的室内定位依赖于空间指纹特征.
- 低分辨率的指纹图像限制了微妙特征的提取,影响了准确性和稳定性.
- 现有的方法难以有效地利用空间信息进行精确的定位.
研究的目的:
- 提出一种改进的基于RSS的室内定位方法,使用增强的空间和频域指纹表示.
- 通过卷积神经网络 (CNN) 利用融合学习来实现更准确的3D坐标预测.
- 为了解决低分辨率空间RSS指纹图像的局限性.
主要方法:
- 应用双立方插值来增强RSS指纹图像的空间分辨率.
- 利用2D快速里埃转换 (2D FFT) 将空间图像转换为频域表示.
- 开发了一个并行CNN,具有高效的多尺度注意力 (EMA) 模块,用于双模 (空间和频率) 功能提取和融合.
- 实施了用于最终3D坐标估计的坐标优化策略.
主要成果:
- 拟议的方法在7个公共数据集的定位准确性方面取得了显著的改进.
- 实现了高达47.1%的平均定位误差降低.
- 与传统和先进方法相比,根平均平方误差 (RMSE) 降低了多达54.4%.
结论:
- 增强的空间频率指纹表示和融合学习的结合有效地提取了室内定位的更丰富的功能.
- 拟议的方法在基于RSS的室内定位准确性和稳定性方面取得了重大进展.
- 这种方法为克服低分辨率空间RSS指纹图像所带来的挑战提供了一个有希望的解决方案.
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