# A CSAR 3D Imaging Method Suitable for Edge Computation
## 4 Data Processing
### Simulation Data Processing
\begin{table}
\begin{tabular}{c c c c} \hline  & **BP (m)** & **IBP (m)** & **The Proposed Algorithm (m)** \\ \hline point A & 0.2 & 0.24 & 0.36 \\ point B & 0.3 & 0.32 & 0.36 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of point target resolution. Figure 6: Two-dimensional imaging comparison of the three methods when height z = 5 m: (**a**) 2D image using the traditional BP algorithm when z = 5 m; (**b**) 2D image using the IBP algorithm when z = 5 m; and (**c**) 2D image using the proposed imaging algorithm when z = 5 m.  
Figure 7: Comparison of point target slices: (**a**) X slices of point A; (**b**) X slices of point B.  
shown in Figure 9d. When x = 5 m, the peak point after the Hough transform is extracted, as shown in Figure 9e, and the intersection point of the extracted straight line and the detected output straight line is shown in Figure 9f. The two intersection points are located at different heights, consistent with heights of z = 0 m and z = 5 m in the scene. Figure 8: Typical height images based on IRT: (**a**) image when the height z = 0 m; (**b**) image when the height z = 2.5 m; and (**c**) image when the height z = 5 m.  
Figure 9: Intersection points extracted from slices at different X positions via Hough transform: (**a**) peak points detected at x = \(-\)5 m; (**b**) end points and intersection points detected at x = \(-\)5 m; (**c**) peak points detected at x = 0 m; (**d**) end points and intersection points detected at x = 0 m; (**e**) peak points detected at x = 5 m; and (**f**) end points and intersection points detected at x = 5 m.  
Taking point targets (\(-5\), \(5\), \(0\)) and (\(-5\), \(-5\), \(0\)) as examples, the intersection points of x = \(-5\) m, x = \(-6.5\) m, and x = \(-8\) m slices are extracted, as shown in Figure 10. As the slice sits away from the target slice x = \(-5\) m, the position of the extracted intersection points moves to the right. That is, the height gradually increases, and the intersection points away from the focusing height, consistent with the analysis in Figure 4b. Using Hough transform, extracting the intersection points of the height slice of the defocused circle in the Y direction is consistent with that in the X direction, so the process will not be repeated. After voting fusion, the threshold is set to 2. The results after extracting the 3D point cloud (Figure 11) demonstrate that the method proposed in this paper can effectively remove CSAR 3D point clouds. However, due to the limitations of resolution in the slant range direction of the system, the target position is not entirely located in the imaging grid, resulting in a deviation of about one grid in the process of extracting intersection points via the Hough transform. Thus, the target points far from the imaging center are scattered to some extent. The above data processing adopted MATLAB R2019b with an i7\(-\)10750H processor. The imaging time consumption of the traditional BP 3D imaging algorithm, the IBP 3D imaging method, and the proposed algorithm was also compared, as shown in Table 3. The time consumption of the proposed algorithm was 582.1 s, which was about half that of the IBP 3D imaging method and one-sixth that of the traditional BP 3D imaging algorithm. \begin{table}
\begin{tabular}{c c c} \hline \hline
**BP (s)** & **IBP (s)** & **The Proposed Algorithm (s)** \\ \hline
3787.4 & 1278.4 & 582.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Time consumption comparison. Figure 11: Result of 3D point cloud extraction. Figure 10: Intersection points extracted from different X position slices via Hough transform for the same target points: (**a**) intersection points detected at x = \(-5\) m; (**b**) intersection points detected at x = \(-6.5\) m; and (**c**) intersection points detected at x = \(-8\) m.