## 3 Method
### Proposed Method
The global CFAR is perform detection on the whole data rather than using the sliding window, because to the sidelobes are mostly eliminated  
Figure 7: Three-view mask inverse projection schematic diagram. in the previous step. In practice, it converts the whole extracted 3D data into a row vector, computing its mean and variance to determine the detection threshold \(I_{c}\), based on the constant false alarm probability. Subsequently, the central pixel \(X_{i}\) of the sliding window is traversed in the potential target area data to detect more accurate target points. The derived formula is presented in Equation (11). \[P(x,y,z)=CFAR[f(x,y,z)]\] (11)
* The modified DBSCAN In response to the uneven cluster density of 3D data target points and their often linear structure in the vertical plane, we have proposed a modification to the conventional DBSCAN clustering algorithm. By imposing more constrained clustering conditions, we aim to obtain more accurate results for such cases. Specifically, we have introduced a change to the shape of the clustering metric from a sphere to a cylinder, as illustrated in Figure 8. This adjustment is particularly useful in addressing scenarios where there may be significant variation in point densities both within and between clusters. In Figure 8, the blue scatter points represent the pixels in the data. The blue sphere and the blue cylinder are the clustering constraint shape of the traditional DBSCAN method and the modified DBSCAN method, respectively. The modified DBSCAN clustering algorithm utilizes three key parameters to describe the compactness of the point cloud distribution, namely, the neighborhood radius \(r\) in the \(x\) and \(z\) planes, the neighborhood distance \(h\) along the y-axis, and MinPts, which sets the minimum number of pixels required to form a cluster, which is the density threshold. These parameters should be tuned to achieve optimal clustering performance. Figure 9 is the diagram of a new clustering constraint for the cylindrical shape template. The red point represents any two points in the data. After derivation, Equation (12) provides a formula for determining the distance between different dimensions, while Equation (13) specifies the constraint conditions. It should be noted that these parameters are currently determined based on empirical experience, but future research may explore more optimal ways to identify suitable parameter configurations that can further enhance performance. Figure 8: Clustering constraint shape diagram. \[ \begin{cases}d_{xz}=\sqrt{(x_{1}-x_{2})^{2}+(z_{1}-z_{2})^{2}}&\text{distance of $x$ and $z$ planes}\\ d_{y}=\sqrt{(y_{1}-y_{2})^{2}}&\text{distance of height}\end{cases}  \tag{12}\]  
\[ \begin{cases}d_{xz}\leq r&\text{distance of $x$ and $z$ planes}\\ d_{y}\leq h&\text{distance of height}\end{cases}  \tag{13}\]  
where:  
\(d_{xz}\): is the distance of x and z planes;  
\(d_{y}\): is the distance of height;  
\(r\): is the cylindrical shape template radius;  
\(h\): is the cylindrical shape template half the height. Our algorithm starts by randomly searching for pixels in the 3D target point cloud \(P(x,y,z)\). For each identified pixel, the algorithm conducts a search within the cylindrical area, applying the previously defined constraints. Any cluster of pixels that contains more Minpts pixels as defined by our clustering conditions is marked and classified accordingly. Pixels that do not fit these criteria are labeled as noise. This process continues until all pixels are either classified into one or more clusters or classified as noise. As we note, the modified DBSCAN algorithm is represented by mDBSCAN in Equation (14). Through this method, we obtain the final clustering result \(D(x,y,z)\), with a precise spatial distribution of points, which suit the 3D CS-GBSAR data. \[D(x,y,z)=mDBSCAN[P(x,y,z)] \tag{14}\]  
After the clustering process, we analyze the resulting data to filter out strong sidelobe classes based on target characteristics such as angle, shape, size and location.