## 2 Related Work  
Research on classification and identification using millimeter-wave radar has been increasing in recent years. Many researchers use millimeter-wave radar to identify human gait [6; 7], gestures [8; 9], and activities [10; 11; 12; 13; 14; 15; 16]. They extract different types of data from the raw data returned by the radar and use machine learning [17] and deep learning to extract the features of the target to achieve the purpose of classification and identification. For example, in [7], researchers collect a large variety of human walking data with millimeter-wave radar and use this data set to evaluate the performance of some deep learning networks. In [8], the researchers discuss the feasibility of AI-assisted gesture recognition using 802.11 ad 60 GHz (millimeter wave) technology in smartphones. They design a CNN + LSTM sequence model and a tiny pure CNN model to recognize different gestures, including hands and fingers, with high precision and small size.  
In the experiments of recognizing human activities, the experimenters use a variety of methods. For example, in [10], the experimenters converted the obtained point cloud data into micro-Doppler features and used CNN to extract the features to classify actions. In [11], the researchers proposed a new point network. They collect human echo signals through radar, extract the range-velocity sequence, and then add the time dimension on the basis of two-dimensional data to form the range-velocity-time data. Compared with directly processing the original point cloud, the point network can learn the structural features from the micromotion trajectory more effectively; that is, it can learn the dynamic motion better. In [12], the researchers find that measurements with very high azimuth and elevation resolution can locate scatterers from the human body. The authors extract features from the point cloud using deep recurrent neural networks with two-dimensional convolutional networks (DRNN). Some researchers are dissatisfied with using a single type of feature for identification but use multiple data as input. In [13], the researchers extracted the time-range, time-Doppler, and range-Doppler features of the radar signal and fused them, and then used CNN for learning and classification and obtained a higher accuracy than the traditional two-dimensional recognition method. Some researchers try to improve the speed of human action recognition. In [14], the researchers propose a field-programmable gate array (FPGA) based convolutional neural network (CNN) acceleration method. They use the spectrogram of the millimeter-wave radar echo as the CNN input and finally find that the method not only maintains a high classification accuracy but also improves the execution speed. Some researchers have built a system framework for higher accuracy. In [15], the researchers propose a hands-free human activity recognition framework leveraging millimeter-wave sensors. They implement the network in one architecture and optimize it to have higher accuracy than those networks that can only obtain a single result (i.e., only pose estimation or activity recognition). Some researchers focus on solving some application problems in the real world. For example, in [16], researchers tried to solve these two problems: poor recognition accuracy in noisy environments and being unable to provide a real-time response due to long time delay. They propose m-activity, a method that can reduce the noise caused by the environmental multipath effect and run smoothly while realizing har. The off-line accuracy and implementation accuracy of this method are verified in the gym, and good results are obtained.