# A New Model for Human Running Micro-Doppler FMCW Radar Features
## 4 Experimental Result  
The sensors are the AWR1642 radar and the Azure Kinect somatosensory camera. The AWR1642 radar system emits FMCW signals. The system consists of three parts: the RF front-end, a data acquisition module, and a signal processing terminal. Continuous sampling of data is achieved through circular calls from memory. Microsoft's next-generation Azure Kinect motion capture system is the most common device in markerless acquisition. It provides an excellent human joint tracking SDK and electronic motion sensors. In the actual measurement, the camera captures the trajectory of the body's joint points. Then the coordinate changes of these joint points are imported into the human model as a function of time, so that the human model and the actual human body achieve a high degree of consistency in running movements. This adds reliability to the subsequent validation.  
Azure Kinect is used to collect 3D data of human bones and joints while running in an unobstructed environment. It can capture the distinguishing characteristics of human bones and joints without markers. An optical motion acquisition system with multiple cameras is used to record the motion of an optical marker placed on the body [30]. Each camera can acquire data in a two-dimensional coordinate system, and it is necessary to keep each optical marker seen by at least two cameras. Based on these two-dimensional coordinate groups, motion data in a three-dimensional coordinate system can be calculated. However, multiple infrared cameras are used to capture and integrate 3D data from multiple angles [30], mainly to solve the instability problem when the single camera is collecting data. Compared to a single camera, this approach has little difference in effectiveness for accessibility [31]. Moreover, the markerless method is easy to prepare, which has low equipment costs, and requires less space than the active marking method. The FMCW radar experimental platform is shown in Figure 4. The experimental site should be spacious and bright. In order to better detect the human target, a specific set of radar parameters was selected for this experiment, where the center frequency was 77 GHz, the sampling frequency was 2.5 MHz, the frequency modulation period was 50 us, the number of pulses per frame was 128, and the bandwidth was 4000 MHz.  
In accordance with the parameters described above, human target detection was performed to obtain echo and micro-motion information. Figure 5 simulates the moving distance information of the human running model. Figure 5 has a color band on the side, and the corresponding color of the band is mapped to the image matrix data, indicating the dynamic range and value change of the data. Through continuous color change from blue to red, the change from low to high values of the distance profile data are visually  
Figure 4: Experimental verification platform for FMCW radar.  
represented. The data show that the human running model moved from 6 m to 2 m from FMCW radar.  
Figure 6 shows the micro-Doppler characteristics obtained from the simulation and the micro-Doppler characteristics of human running in the actual detection situation. It uses the short-time Fourier transform (STFT) method. As can be seen from Figure 6, the modulation of the echoes by the legs, feet, and arms is reflected in the outer envelope of the time-frequency spectrum. The strongest part is the modulation information of the torso. The modulation information of other body parts fluctuates periodically around the torso and forms the inner envelope of the time-frequency spectrum.