# Stationary Target Identification in a Traffic Monitoring Radar System
## 5 Conclusions  
In this paper, we proposed a method for discriminating stationary targets in the traffic monitoring radar system. The signal processing algorithm that automatically identifies stationary road structures is a function that must be implemented in the traffic monitoring radar system. If a stationary structure is recognized as a vehicle and this information is delivered to the driver on the road, a dangerous traffic accident can occur. We first collected raw radar data by mounting our designed FMCW radar sensor using the 24 GHz band on a road structure, such as an overpass. Then, the raw data were processed to extract various information of the targets on the road, such as the distance, velocity, angle, and radar cross-section (RCS). To classify stationary and moving targets, we used the angle-velocity domain characteristics of the targets. Unlike in the two-dimensional \(x\)-\(y\) range map, stationary targets existed around the straight line with zero velocity in the angle-velocity domain. Thus, in this domain, there is the advantage that moving targets and stationary targets are clearly distinguished. Finally, to find the inliers on the straight line, we used the MSAC algorithm and verified its performance using the actual radar measurement results. As a result of checking the slopes of the estimated trend lines in successive frames, the estimation accuracy of the proposed method was higher compared to the LLS-based method because it does not estimate the trend line using all detected points. In addition, the proposed identification method has an advantage in that it can be applied in real time due to its small amount of calculation. We believe that the proposed method is essential for stable detection performance in traffic monitoring radar systems. Including discriminating stationary targets, we plan to conduct research on how to classify the types of moving vehicles, such as sedans, sports utility vehicles, or trailers.  
Conceptualization, J.-E.L. and S.L.; methodology, H.-S.L. and S.L.; software, H.-S.L. and S.L.; validation, H.-S.L. and S.L.; formal analysis, H.-S.L.; investigation, H.-S.L. and S.L.; resources, H.-M.P. and J.-E.L.; data curation, H.-S.L. and S.L.; writing, original draft preparation, H.-S.L. and S.L.; writing, review and  
Figure 12: Flowchart of the proposed method (inside the DSP).  
editing, H.-M.P. and S.L.; visualization, H.-S.L. and S.L.; supervision, J.-E.L. and S.L.; project administration, J.-E.L. and S.L.; funding acquisition, J.-E.L. and S.L. All authors read and agreed to the published version of the manuscript.  
This work was supported by the 2020 Korea Aerospace University Faculty Research Grant.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADC & Analog-to-digital converter \\ DSP & Digital signal processor \\ FMCW & Frequency-modulated continuous wave \\ FOV & Field of view \\ GSM & Global System for Mobile Communications \\ ITS & Intelligent transportation system \\ LLS & Linear least squares \\ LPF & Low-pass filter \\ MSAC & M-estimator sample and consensus \\ RANSAC & Random sample consensus \\ RCS & Radar cross-section \\ VCO & Voltage-controlled oscillator \\ \end{tabular}