CAEFi:基于卷积自编码器降维的信道状态信息指纹室内定位方法

CAEFI:Channel State Information Fingerprint Indoor Location Method Using Convolutional Autoencoder for Dimension Reduction

针对提高Wi-Fi指纹室内定位技术性能,该文首先提出一种基于卷积神经网络(CNN)的信道状态信息(CSI)指纹室内定位方法。该方法在离线阶段联合CSI幅度差和相位差信息对CNN模型进行训练。在廊厅和实验室两种不同室内定位场景进行了定位实验,分别获得了25 cm和48 cm的平均定位误差;然后,在此基础上重点针对提高基于CNN的CSI室内定位时效性,引入卷积自编码器(CAE)实现CSI的降维处理,在保证原始定位方法精度的前提下,定位时间提高了40%,同时将内存消耗降低到原算法的1/15,实验结果验证了所提算法的有效性。

In order to improve the performance of Wi-Fi fingerprint indoor positioning technology,a method based on Convolutional Neural Networks(CNN)for Channel State Information(CSI)fingerprint indoor positioning is first proposed.This method combines the CSI amplitude difference and phase difference information to train the CNN model in the offline stage.Positioning experiments are carried out in two different indoor positioning scenarios in the gallery and the laboratory,and the average positioning errors of 25 cm and 48 cm are obtained respectively;Then,on this basis,the focus is on improving the timeliness of CNN-based CSI indoor positioning.The Convolutional AutoEncoder(CAE)is introduced to realize the dimensionality reduction processing of CSI.Under the premise of ensuring the accuracy of the original positioning method,the positioning time is increased by 40%and the memory consumption is reduced to 1/15 of the original algorithm.The experimental results verify the effectiveness of the proposed algorithm.