摘要
针对卷积神经网络(convolutional neural network,CNN)计算复杂度高和内存占用大的问题,本文提出了一种基于轻量级CNN的信道状态信息(channel state information,CSI)图像指纹被动定位(LCNNLoc)方法.离线训练阶段,将幅值差矩阵和相位矩阵构造成类似于“RGB”的三通道特征图像;同时设计了一个轻量级CNN架构,利用特征图像作为该框架的输入进行训练,在训练结束时将CNN模型保存为指纹数据库.在线定位阶段,采用概率加权质心方法实现了实时的位置估计.实验结果表明,相较于传统方法,LCNNLoc不仅提升了定位精度,还降低了算法运行耗时.
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.
作者
黄良璜
余敏
HUANG Lianghuang;YU Min(College of Computer Information and Engineering,Jiangxi Normal University,Nanchang 330022,China)
出处
《全球定位系统》
2025年第1期41-47,共7页
Gnss World of China
基金
中央引导地方科技发展资金跨区域研发合作项目(20222ZDH04090)
江西省教育厅研究生创新基金项目(YC2022-s350)。
