基于智能手机CSI的轻量级指纹定位模型
A Lightweight Fingerprinting Localization Model based on Smartphone CSI
- 物联网学报 2026年
- 作者机构：
1.内蒙古大学计算机学院 呼和浩特 010021
2.生态大数据教育部工程研究中心 呼和浩特 010021
3.内蒙古电子科技有限责任公司 鄂尔多斯 017000
- 作者简介：
黄宝琦，cshbq@imu.edu.cn
- 基金信息：国家自然科学基金项目(62262046;42161070);内蒙古科技计划项目(2022YFSJ0027);鄂尔多斯市科技计划项目(YF20240029);呼和浩特市基础研究与应用基础研究项目（No.2025-规-基-42）
DOI：XXX
中图分类号： TP393收稿：2025-06-14，
修回：2025-08-08，
录用：2025-09-01，
- 稿件说明：
移动端阅览
杨润泽, 黄宝琦, 贾冰. 基于智能手机CSI的轻量级指纹定位模型[J/OL]. 物联网学报, 2026.
YANG Runze, HUANG Baoqi, JIA Bing. A Lightweight Fingerprinting Localization Model based on Smartphone CSI[J/OL]. Chinese Journal on Internet of Things, 2026.
摘要
近年来，深度学习技术被广泛地应用于基于信道状态信息(Channel State Information，CSI)的指纹定位领域，并展现出高可靠的定位精度。然而，基于深度学习的指纹定位方法大多依赖于多层次的网络模型结构来提取具有判别性的位置特征，此过程伴随着大量的模型参数和密集的计算操作，对设备的硬件资源占用较高。特别是对于资源有限的智能手机而言，这会为其带来不容忽视的负担。为此，本文提出一种新颖的智能手机CSI轻量级指纹定位模型，（1）通过设计简洁高效的特征提取模块，仅用少量参数的线性层与卷积层结合特征融合机制，兼顾模型轻量化与特征提取能力；（2）引入数据增强模块，通过生成非参考点CSI数据，扩充训练样本空间，显著提升模型对未经训练位置的鉴别能力。本文在两种典型的室内场景下开展实验，与最优的基准模型相比，本文提出的定位模型在两种场景下的平均RMSE降低了12.5%，并且单次定位的推理时间仅为0.08秒。实验结果表明，本文提出的定位模型不仅能够有效提高定位精度，还明显缩短了定位所需时间。
Abstract
In recent years
deep learning technologies have been widely adopted to the Channel State Information (CSI)-based fingerprinting localization field
demonstrating reliable localization accuracy. However
deep learning-based fingerprinting localization models typically rely on multi-layered network structures to extract distinctive location features
the process accompanied by a large number of model parameters and intensive computational operations
which occupy substantial physical resources on devices. This can be a significant burden
especially for resource-limited smartphones. To this end
this paper proposes a novel lightweight fingerprinting localization model based on smartphone CSI，(1) a concise and efficient feature extraction module is designed
employing linear and convolutional layers with a minimal number of parameters and incorporating a feature fusion mechanism to achieve both lightweight deployment and strong discriminative capability in feature extraction; (2) a data augmentation module is introduced to generate CSI data for unknown points
thereby expanding the training sample space and significantly enhancing the model’s recognition ability for untrained locations. Experiments were conducted in two typical indoor scenarios. The results show that compared to the most advanced benchmark models
the proposed localization model has reduced the average RMSE by 12.5% in two indoor scenarios
and the inference time for a single localization is only 0.08 seconds. The proposed localization model not only effectively improves positioning accuracy but also significantly reduces the required positioning time.
关键词
Keywords
references
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