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室内WiFi指纹定位基于叠加自动编码器和多标签分类的双人群PSO和多标签分类

Yiguo Cheng¹, Kun Zhang², Bing Zheng³

¹School of Information Science and Technology, Hainan Normal University, Haikou, 571158, Hainan, China.

Scientific reports

|September 26, 2025

概括

这项研究引入了一个新的深度神经网络框架,用于WiFi指纹,提高室内定位的准确性. 双队列粒子队列优化在复杂环境中显著改善了定位和地板预测.

科学领域:

计算机科学计算机科学
电气工程电气工程
人工智能的人工智能

背景情况:

无线网络指纹是一个具有成本效益的室内定位方法.
在复杂的多层,多建筑环境中,由于信号干扰和结构复杂性,准确性受到限制.

研究的目的:

为了提高WiFi指纹的准确性,用于室内定位.
解决复杂环境中的局限性,使用全新的深度学习框架.

主要方法:

一个深度神经网络 (DNN) 框架,集成堆叠自动编码器 (SAE) 用于特征提取.
使用接收信号强度 (RSS) 进行本地化的一种多标签分类策略.
双队列粒子队列优化 (DSPSO) 用于优化高维模型参数,增强全球探索并避免局部优化.

主要成果:

在基准函数上,DSPSO的表现优于传统的粒子群优化 (PSO) 和搜索算法 (SSA).
在DSPSO优化的SAE-DNN模型中,平均定位误差为9.42m,比支向量回归 (SVR) 有13.74%的改善.
该模型在建筑物识别中显示了100%的准确性,在地板预测中显示了92.97%.

结论:

拟议的DSPSO优化的SAE-DNN框架显著提高了室内定位的准确性.
这种方法对于多个建筑,多层的本地化挑战是有效的.
这种方法提供了一个强大的解决方案,用于精确的室内定位,使用WiFi信号.

关键词:

深度神经网络模型模型双人群-粒子群集优化双人群-粒子群集优化多标签分类是多标签分类.接收到的信号强度.堆叠的自动编码器无线网络指纹定位定位

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