Multidimensional Information Recognition Algorithm Based on CSI Decomposition

Wireless sensing technology based on channel state information (CSI) has broad application prospects in human–computer interaction, smart homes, and other fields due to its advantages, which include no special equipment deployment, no privacy leakage, and no light intensity and line of sight influence. The existing studies have achieved satisfactory recognition accuracy for human localization or activity information. However, many applications need to recognize not only the activity of humans but also the location of humans. Therefore, multidimensional information recognition for human targets has become an urgent problem to be solved. For this problem, a multidimensional information recognition algorithm for human targets based on CSI decomposition (called the CD-MDIR algorithm) is proposed. Specifically, we first decompose the CSI time series into dynamic location CSI (DLC) components affected by human location and dynamic activity CSI (DAC) components affected by human activity, according to the independent characteristics of the influence of human location and activity on CSI. Then, the linear discriminant analysis (LDA) algorithm is used to transform the DLC component to enhance the location information, and the features of the DAC component are extracted to enhance the activity information. Finally, we designed a long short-term memory (LSTM) multidimensional information recognition network that successively recognizes the location and activity information of humans. Experimental results show that the proposed CD-MDIR algorithm achieves both higher localization and activity recognition accuracy.