Gesture Recognition Algorithm Based on WiFi and MMWR Fusion

The environment of gesture recognition is complex and changeable. The wireless fidelity signal can accurately be recognized in the non-line-of-sight environment and the millimeter wave radar signal has a strong anti-interference ability. This paper absorbs the advantages of two platforms and proposes a fusion neural network based on wireless fidelity and millimeter wave radar gesture recognition algorithm. The experimental results show that the algorithm can effectively improve the accuracy and robustness of the gesture recognition system. This paper’s contribution is using gate recurrent unit and attention mechanism to extract the depth features of channel state information data and compressed range-Doppler map respectively which are based on the data characteristics of signals collected by different platforms after signal processing. They improve the overall performance of the gesture recognition system.