## 2 Micro-Doppler Signals
### Data Acquisition  
Figure 5 shows the experimental setup used to gather signals. The test site is an open space with no obstacles. We considered micro-Doppler signals caused by four types of background noise (Line of sight (LoS), fan, snow and rain). These noise signals can be understood as common background noises that can frequently be received in real outdoor environments. We additionally considered human walking micro-Doppler signals combined with these four types of background noise; that is, we gathered the eight types of signals in total whose examples are shown in Figure 6. The Doppler radar was installed at a selected test site and raw data were gathered for several days. In particular, to see rain and snow effects, we collected a rain and snow background noise dataset.  
To measure signals generated by human walking motions, the experimenter only moved within the radar detection area. We set the detection area through the pre-test and marked the boundary on the ground. Three adults (height: 173 cm, 177 cm, 182 cm) participated as the experimenter. The experimenter selected and moved the direction that the experimenter wanted within the detection area. We consider only human walking motions of regular walking and fast walking as general cases. These walking movements are the movements that we use in our daily life. Unusual human movements, such as crawling and irregular movements without direction and purpose, were excluded. To collect Doppler signals caused by background noises, we used a limited experimental environment to avoid mixing different background noises. The fan signals were collected using two types of ordinary electric fans for home use. The fan signal was measured at a distance of 5 m from the radar.  
Figure 4: Twenty-four GHz Doppler radar sensor board implemented in this research. (**a**) Front; (**b**) Back.  
Figure 3: Block diagram of the low-cost & short-range 24 GHz Doppler Radar.  
Figure 5: The experimental setup: the radar was installed outdoors to collect various background noise.  
Figure 6: The micro-Doppler spectrograms of eight types. These show the spectrogram images during about 90 s. From these spectrograms, we can understand the frequency characteristics of each class. The signal by an electric fan has harmonic frequencies and the signals by snow and rain are relatively broadband frequency characteristics. In the case of the human walking signal, it can be aware that the signal intensity returned by torso is stronger than other parts. Therefore, it shows that it is possible to classify classes using the frequency characteristics of a signal instead of spectrogram images. (**a**) Outdoor environment (LoS); (**b**) Fan; (**c**) Snow; (**d**) Rain; (**e**) Human walking; (**f**) Human walking with fan; (**g**) Human walking with snow; (**h**) Human walking with rain.  
The experiments under clear weather conditions were done for about three days and micro-Doppler signals for rain and snow were collected for two days each on the day when the weather conditions were met. The amount of rainfall and snowfall was according to the weather forecast and we measured rain and snow signal under moderate rainfall and snowfall conditions. We carefully notify that this experiment does not cover all weather conditions.