# Target Classification Using Frontal Images Measured by 77 GHz FMCW Radar through DCNN
## 6 Conclusions  
In this study, we investigated the feasibility of classifying targets through frontal imaging. Rather than exploring signatures in the range profile, range-Doppler or spectrogram domain, the frontal images constructed along with azimuth and elevation served as key a feature in the classification process. In the synthesis of the frontal image, several frontal images corresponding to each bin in the range profile are overlapped. DCNN was exploited as a classifier and was trained by the stochastic gradient descent with a momentum optimizer. The confusion matrix revealed that the classification accuracy for sedans, SUVs, trucks, trees, poles, and humans yielded up to 87.1%. If the classifier was designed to classify vehicles, humans, and clutter, the classification rate increases to 92.6%. However, it should be noted that the classification accuracy using the proposed method is not higher than that of other methods such as the use of the range-Doppler domain. The results showed that the frontal image can potentially serve as one of the modalities in the target classification scheme, yet high spatial-resolution radar should be developed, or other features can also be incorporated together to maximize performance.  
Conceptualization, Y.K., S.Y. and B.J.J.; methodology, Y.K. and S.Y.; software, M.E.; validation, M.E. and B.J.J.; formal analysis, M.E.; writing--original draft preparation, M.E. and Y.K.; writing--review and editing, Y.K. and S.Y.; visualization, M.E.; supervision, S.Y. and B.J.J.; project administration, S.Y. and B.J.J.; funding acquisition, S.Y. and B.J.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by an Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government [22 ZH1100, Study on 3D Communication Technology for Hyperconnectivity] and the National Research Foundation (NRF), Korea [BK21 FOUR].  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of California State University, Fresno.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fold 1** & **Fold 2** & **Fold 3** & **Fold 4** & **Fold 5** & **Ave** \\ \hline
87.17\% & 88.64\% & 82.60\% & 88.28\% & 88.83\% & 87.10\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of 5-fold validation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**True/Est** & **Sedan** & **SUV** & **Truck** & **Tree** & **Pole** & **Human** \\ \hline Sedan & 365 & 14 & 30 & 20 & 14 & 12 \\ SUV & 24 & 387 & 15 & 14 & 15 & 0 \\ Truck & 6 & 3 & 441 & 4 & 1 & 0 \\ Tree & 7 & 2 & 0 & 416 & 27 & 3 \\ Pole & 16 & 1 & 0 & 32 & 396 & 10 \\ Human & 8 & 14 & 0 & 16 & 44 & 373 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix.  
**Informed Consent Statement**: Informed consent was obtained from all subjects involved in the study. Written informed consent was obtained from the subjects to the publication of this paper.  
**Data Availability Statement**: Not applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.