Research on heartbeat recognition of millimeter wave radar based on LSTM

In order to solve the problem of poor concealment and easy forgery of traditional authentication, and ensure good identification effect. We propose contactless authentication using heartbeat information collected by millimeter wave radar. The Frequency Modulated Continuous Wave (FMCW) signal is transmitted to the human heart by millimeter Wave radar, and the IF signal is extracted by mixing the echo signal. The phase of IF signal is extracted, and the phase difference of human heartbeat is obtained by frame difference. A deep learning network model including input layer, Long Short Term Memory (LSTM) layer, fully connected layer, softmax layer and classification output layer was constructed. Wavelet scattering is used to automatically extract features from heartbeat data extracted by millimeter wave radar. The LSTM model is trained and tested with the extracted features, and the recognition results are output. This method has achieved good recognition effect, and the recognition accuracy of three people can reach 83.8%.