## 1 Introduction  
Radar sensors are relatively robust to weather conditions and have the advantage of being able to detect large areas. In particular, it can be applied as a helpful sensor for detecting humans in outdoor conditions for security purposes. For detection, Doppler signals of radar are essential information to filter moving targets under environments with complicated background noise. For air surveillance radar, since the speed of a target is very fast, it may be not challenging to classify target signals from background noises. However, human walking motions induce complex frequency modulation that includes the Doppler shift by a torso and the additional Doppler shifts of the side-band in the returned signal; this phenomenon is known as the micro-Doppler effect [1]. In particular, since the speed of human walking is much slower than that of airplanes or vehicles, classifying micro-Doppler signals by humans walking from background noise signals can be a severe problem. Since the bandwidth of micro-Doppler signals of human walking motion can overlap the bandwidth of micro-Doppler signals of background noise, it can be challenging to filter two signals individually. Figure 1 describes the difficulty of detecting human walking in an outdoor environment. Processing techniques that analyze or recognize patterns of received signals can be useful approaches to overcome these limitations. Much research has investigated detection problems in Doppler radar signals using micro-Doppler signatures or pattern recognition techniques. According to the reported studies, many researchers have described or proposed mathematical equations or approaches for extracting a meaningful feature vector that can affect the classifier's estimation accuracy [2; 3; 4; 5; 6; 7]. Similarly, other studies tried to find meaningful features through statistical analysis [5; 6; 7; 8]. These approaches need a relatively long pre-processing time (window time) to extract a feature vector from the micro-Doppler spectrogram. For example, the authors of Reference [2] reported that six features are extracted by processing data over a three-second period. Another work [3] analyzed the classification accuracy according to the dwell time and reported that three or four seconds were required to get 90% accuracy. Since this window time directly affects the response and update speed of a radar sensor, a radar sensor's response speed is lowered as we increase this window time. Also, References [9; 10] showed that a model having parameters or mathematical equations could be used to formalize micro-Doppler signals of human walking motion and the virtual features of a human walking motion can be generated. However, making a model that can cover all human activities and background noise may be challengeable. Also, statistical analysis or modeling of highly variable micro-Doppler signals for extracting an essential feature vector may be quite challenging approaches. Instead of these previous approaches, recent deep learning gradually reconstructs and generates features through a process of passing several hidden layers in deep networks. If we design deep learning well, we can easily extract a feature vector without complex calculations for pre-processing or simulation models. Recent studies related to the convolutional neural network, which is a type of deep learning methods, have been researched for recognizing micro spectrogram signals and they showed improved classification accuracy by deep learning technology [11; 12; 13]. However, since the long window time is still required to obtain micro-Doppler spectrogram images, it may negatively affect the sensor's response speed. For example, there are limitations to the application of these approaches in short-range sensors that need to recognize targets quickly.