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 (OCAE-SOM) 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.