摘要
针对室内定位技术精度较低及数据量过大影响运算时间等问题,提出基于OCAE-SOM(Optimized Convolutional Autoencoder-Self Organizing Map)的室内指纹定位算法。离线阶段,先将信道状态信息的幅值相位预处理矩阵作为原始输入数据,并调整为RGB(Red,Green,Blue)格式训练卷积自编码器,使其可深度挖掘参考点的指纹特征,采用Adam算法优化CAE算法的参数,既降低数据维度又能提升训练效率;然后采用OCAE-SOM算法训练模型,可以缩短单独训练模型的时间;最后采用Adam算法优化SOM的权重,可较好地保留输出特征间的相关性,避免权重参数出现局部最优。在线阶段,将调整后的测试数据输入到OCAE-SOM算法中,经匹配后可得到输出位置点。实验结果表明,该算法模型在定位时间与精度上显著优于已有算法,具有一定的应用价值。
Aiming at the problems of low accuracy of indoor positioning technology and computational complexity,an indoor fingerprint location algorithm based on optimized convolutional autoencoder-self organizing map(OCAESOM)is proposed.In the offline stage,first,we use the amplitude and phase-preprocessing matrix of a channel state information as the original input data and adjust it to the RGB format to train the convolutional autoencoder(CAE)algorithm so that it can deeply mine the fingerprint features of a reference point.The Adam algorithm is employed to optimize the parameters of the CAE algorithm,which not only reduces the data dimension but also improves training efficiency.Then,we use the OCAE-SOM algorithm for model training.It can shorten the time to train the model separately.Finally,we use the Adam algorithm to optimize the weight of the self-organizing map,which can be better retain the correlation between output features to avoid the local optimization of weight parameters.In the online stage,the adjusted test data are input into the OCAE-SOM algorithm,and the output location point is obtained after matching.The experimental results show that the OCAE-SOM algorithm is significantly better than existing algorithms in terms of positioning time and accuracy,and it has certain application values.
作者
李新春
纪小璐
魏武
王藜谚
谷永延
曹大焱
Li Xinchun;Ji Xiaolu;Wei Wu;Wang Liyan;Gu Yongyan;Cao Dayan(School of Electronic and Information Engineering,Liaoning Technical University,Huludao,Liaoning 125105,China;Graduate School,Liaoning Technical University,Huludao,Liaoning 125105,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2021年第8期296-306,共11页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61372058)。
关键词
测量
信道状态信息
室内定位
卷积自编码器
自组织映射
Adam算法
measurement
channel state information
indoor localization
convolutional autoencoder
self-organizing map
Adam algorithm
