Gesture Recognition Method Based on Doppler Radar Technology
Jingbo Sai, Na Liu, Anyu Yu · 2021
This paper presents a LSTM neural network gesture recognition method based on Doppler radar technology. Firstly, the velocity and distance of the gesture target are obtained by 2D-FFT, and the antenna array expansion is realized by using the high-order cumulant to estimate the azimuth of the recognition target. Then, the relationship among distance, velocity, azimuth and time is arranged to form a data set. At the same time, a multi-LSTM fusion network model is proposed. Finally, in order to improve the accuracy of recognition, the influence of initial learning rate and the number of hidden nodes on the performance of multi-LSTM fusion network model is analyzed. The experimental data show that when the initial learning rate is 0.005 and the hidden layer node is 128, the average correct rate of the six gestures can reach 96 %.