Many existing indoor localization systems use RSS as fingerprints, but RSS is a coarse-grained data, which not only fluctuates over time but also is not unique for a specific location due to rich multi-path effects and shadow fading in indoor environments. In order to further improve the localization accuracy, a robust and accurate indoor localization algorithm based on deep auto-encoder network combine with multi-feature fusion. To extract deep features hidden in CSI data and reduce the computational complexity of localization, an improved method is designed to achieve dimension reduction and feature extraction, which can avoid explicit information extraction of available CSI characteristics on the basis of effective representation of CSI fingerprint difference between different locations. Then, the fingerprint database is constructed by combination of RSS and CSI coding. Moreover, the Naive Bayer Classifier is adopted to improve the localization accuracy and stability. In the simulation experiment, the positioning effect of the proposed algorithm under different fingerprint libraries and the positioning effect under different positioning methods are mainly carried out. Experimental results show that the proposed method can effectively perform indoor positioning and has good practicability in the actual production environment.

许多现有的室内定位系统都使用RSS作为指纹，但是RSS是一种粗粒度的数据，由于室内环境中丰富的多径效应和阴影褪色，它不仅会随时间波动，而且对于特定位置也不是唯一的。为了进一步提高定位精度，基于深度自动编码器网络结合多特征融合的鲁棒，准确的室内定位算法。为了提取隐藏在CSI数据中的深层特征并降低定位的计算复杂度，设计了一种改进的方法来实现降维和特征提取，在有效表示CSI指纹之间的差异的基础上，可以避免显式提取可用CSI特征的信息。不同的位置。然后，指纹数据库是通过RSS和CSI编码相结合而构建的。此外，采用朴素拜耳分类器以提高定位精度和稳定性。在仿真实验中，主要实现了该算法在不同指纹库下的定位效果和在不同定位方法下的定位效果。实验结果表明，该方法可以有效地进行室内定位，在实际生产环境中具有良好的实用性。主要实现了该算法在不同指纹库下的定位效果和在不同定位方法下的定位效果。实验结果表明，该方法能够有效地进行室内定位，在实际生产环境中具有良好的实用性。主要实现了该算法在不同指纹库下的定位效果和在不同定位方法下的定位效果。实验结果表明，该方法能够有效地进行室内定位，在实际生产环境中具有良好的实用性。