# Target Classification Using Frontal Images Measured by 77 GHz FMCW Radar through DCNN
## 1 Introduction  
Radar has been extensively used in anti-collision driving safety systems because it can detect a target and estimate its range. In the future, advanced radar technology is expected to become an indispensable component of self-driving vehicles [1, 2]. Even though many sensors such as camera lidar and ultrasound are available for automotive applications, radar presents great potential, as it can detect and track a target and provide range information regardless of lighting and weather conditions. In addition, target classification is possible by capturing radar target features.  
For target classification, the radar features were investigated in diverse domains. In [3], the range profile was studied to identify target kinds. One of the most commonly used domains for the classification was the range-Doppler domain [4, 5, 6]. In those studies, the signatures in the range-Doppler were captured and classified by deep learning algorithms. In [7], time-varying features were considered through recurrent neural networks. Spectrogram has been extensively investigated for the classification of non-rigid body motions [8, 9, 10]. Conversely, the development of a large array enabled the use of the point cloud model on a target for classification purposes [11, 12]. Point clouds can provide a 3D shape of a target. However, the frontal image is based on reflection from parts of a target and has not been fully exploited.  
In this paper, we investigate the feasibility of classifying targets through frontal images measured by multiple-input multiple-output (MIMO) radar. In [13], the radar frontal image was constructed through Doppler radar by visualizing the parts of a target that are moving. This study employed MIMO radar to emulate a large array using the concept of a virtual array [14, 15]. The constructed array has a narrow beam width, so the reflection from each part of a target can produce a frontal image by scanning the beam. Therefore, we propose using the frontal image for target classification purposes. The frontal image can include critical information such as the physical shape of a target as well as its reflection characteristics, which can serve as a feature for target classification. The originality ofthis approach is that the target classification is performed using 2D frontal images rather than other radar images that have been used before such as range-Doppler diagrams and spectrograms. As the frontal image is information in a new domain that provides the continuous reflection information of all parts of the target, the reflection distribution depending on the target part becomes target features for classification. On the other hand, the point cloud model was based on 3D spatial information in locations of the strong scattering points of a target, which is discrete information. We employ deep convolutional neural networks (DCNN) to classify the radar frontal images. The general diagram of processing is shown in Figure 1. In this paper, the concept of radar frontal image, radar signal processing, measurement campaign, DCNN structure, and results are presented.