# A CSAR 3D Imaging Method Suitable for Edge Computation
## 4 Data Processing
### Measured Data Processing  
In this section, measured echo data are used to analyze the proposed algorithm's performance. Here, the radar equipped on an SRUAV transmits FMCW signals with a carrier frequency of 9.6 GHz and bandwidth of 750 MHz. The flight radius of the SRUAV is 600 m, the flight altitude is 300 m, and the flight speed is 7 m/s. There are 179,520 slow-time sampling points in the flight circle and 1502 fast-time sampling points. The size of the imaging scene is \(100\times 50\times 7\) m\({}^{3}\), which is divided into \(401\times 201\times 16\) 3D imaging grids based on the range resolution of the echo. The center of the scene is a house composed of scattered containers, as shown in Figure 12a; the flight path is shown in Figure 12b. Due to the limitations of system performance, the three-axis self-stabilizing platform of airborne CSAR is closed. Therefore, the beam center direction of CSAR changes with the attitude of the SRUAV platform and cannot be guaranteed to point only to the observation center.  
The scattering characteristics of containers change with the observation angle. Using the sinusoidal track ridge of 360\({}^{\circ}\), the whole aperture is evenly divided into eight non-overlapping sub-apertures, and the sub-aperture images are obtained by IRT. The full aperture image is obtained by GLRT between the sub-aperture images. The full aperture images of different heights constitute the CSAR image volume. Hough transform is used to extract the intersection points of the defocusing ring height slices at different positions in the X and Y directions, and the voting fusion threshold is set as 10. The results of extracting the CSAR 3D point cloud are shown in Figure 13.  
Figure 12: CSAR experiment scene and flight trajectory: (**a**) optical image of the scene center; (**b**) flight trajectory of the SRUAV using the airborne recorder.  
Figure 13: Three-dimensional point cloud extraction results of the measured scene: (**a**) 3D imaging results of the method proposed in this paper; (**b**) top view of the 3D point cloud using the traditional BP algorithm; (**c**) top view of the 3D point cloud using the IBP algorithm; and (**d**) top view of the 3D point cloud using the proposed method.  
The reconstructed geometric shape of the container house in the center of the scene is shown in Figure 13a. The height of the second floor is 6.36 m, which is consistent with the height of the actual scene. Because there are many containers on the first floor that influence each other, the geometric shape of the point cloud on the first floor is seriously distorted. However, this method can still provide a reasonable estimation of the height. Figure 13b-d shows that compared to traditional BP and IBP 3D imaging methods, the proposed method offers a better 3D focusing effect.  
The above data processing was performed using 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 were also compared, as shown in Table 4. The time consumption of the proposed algorithm was 3809.32s, which was about half that of the IBP 3D imaging method and one-sixth that of the traditional BP 3D imaging algorithm.