CSI Indoor Location Method Based on Stacked Sparse Auto-encoder and SVM

In response to the problem of data redundancy and resolve complex of the indoor location based on high fine-grained channel state information (CSI), when using CSI as fingerprint, a CSI indoor location method based on stacked sparse auto-encoder and support vector machine (SVM) was proposed. First, the amplitude and phase data of physical layer channel information were combined, and the stacked sparse auto-encoder was used to extract the deep location features in the nonlinear fingerprint feature space. Then, sparse feature fingerprint was generated and target location was determined by support vector classifier. The application of sparse feature fingerprint reduces the size of CSI fingerprint database by about 92.6%. Meanwhile, experimental results show that the proposed method can achieve an average positioning error of 1.205 m in a complex indoor environment with mixed line-of-sight and non-line-of-sight propagation paths, and the positioning accuracy is significantly improved compared with other methods.