全球定位系统2025，Vol.50Issue(1)：41-47,7.DOI:10.12265/j.gnss.2024058
基于轻量级卷积神经网络的CSI图像室内定位
Lightweight convolutional neural network-based indoor localization of CSI images
摘要
Abstract
Aiming at the problem of high computational complexity and large memory occupation of convolutional neural network(CNN),this paper proposes a lightweight CNN-based passive localisation method for channel state information(CSI)image fingerprints(LCNNLoc).In the offline training stage,the amplitude difference matrix and phase matrix are constructed into a three-channel feature image similar to"RGB";at the same time,a lightweight CNN architecture is designed,the feature image is used as the input to train the framework,and the CNN model is saved as a fingerprint database at the end of training.In the online positioning stage,real-time position estimation was achieved using a probability weighted centroid method.The experimental results show that compared with the traditional method,LCNNLoc not only improves the positioning accuracy,but also reduces the algorithm running time consuming.关键词
卷积神经网络(CNN)/信道状态信息(CSI)/图像指纹/轻量级网络/概率加权质心方法Key words
convolutional neural network(CNN)/channel state information(CSI)/image fingerprinting/lightweight network/probability weighted centroid method分类
天文与地球科学引用本文复制引用
黄良璜,余敏..基于轻量级卷积神经网络的CSI图像室内定位[J].全球定位系统,2025,50(1):41-47,7.基金项目
中央引导地方科技发展资金跨区域研发合作项目(20222ZDH04090) （20222ZDH04090）
江西省教育厅研究生创新基金项目(YC2022-s350) （YC2022-s350）
