基于轻量级卷积神经网络的CSI图像室内定位OA
Lightweight convolutional neural network-based indoor localization of CSI images
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.
黄良璜;余敏
江西师范大学计算机信息工程学院,南昌 330022江西师范大学计算机信息工程学院,南昌 330022
测绘与仪器
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
convolutional neural network(CNN)channel state information(CSI)image fingerprintinglightweight networkprobability weighted centroid method
《全球定位系统》 2025 (1)
41-47,7
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)
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