## 7 Recognition  
_CNN Model for Feature Extraction and Classification_  
In this section, the proposed CNN model for multiple-target feature extraction and classification is presented for recognizing drone or non-drone targets. To overcome the non-uniformity in the number of points per frame and ensure a consistent length of input data, the data were processed before training and testing the CNN model. Regardless of the number of points in each frame, the point clouds in a 3D point cloud grid were converted into 2D occupancy grids. In this paper, we adopted the algorithm proposed in [49]. In particular, the cluster that encloses the points of a potential drone target was used to determine discriminative spatiotemporal patterns for each target individually.  
However, a CNN model was periodically developed for spatiotemporal feature extraction throughout the 2D occupancy grids. To reduce network consumption and enhance training speed, the features of the point cloud data were directly used as the input data for the CNN, rather than mapping the point cloud to the images. Features extracted from the 2D occupancy grids of the cluster cloud points included the distance, velocity, and angle, as described in Section 4. Additional discriminative features were extracted, such as the height of the target and the size of the clusters.  
The most-distinguishing features of drones are their ability to reach higher altitudes and their smaller sizes compared to pedestrians and other on-ground moving vehicles. The target altitude is determined by calculating the vertical distance as follows:  
\[\lambda=r\;*cos(\theta), \tag{22}\]  
where \(\lambda\) is the height of the target from the ground.  
The target size is determined by calculating the area of the clustered box as follows:  
\[a=2 lw\;*\;2 lh\;*\;2 wh, \tag{23}\]  
The CNN model consists of seven layers, as shown in Figure 8. Layer 1 is the input layer containing the six attributes: radial distance in meters, velocity in meters/second, azimuth angle in degrees, elevation angle in degrees, height in meters, and area in \(meter^{2}\). Layer 2 is made up of six distinct modules, each of which is made up of a spatiotemporal convolution with kernel size \(7\times 7\), maximum pooling composition with a sum size \(3\times 3\), rectified linear unit (ReLU) activation functions, and maximum pooling with pooling area size \(3\times 3\) and a stride of two. Layer 3 is made up of six separated ResNet50. Layer 4 is made up of spatiotemporal convolution with kernel size \(3\times 3\) and average pooling with a pooling area of size \(2\times 2\) to fuse the six features. Layer 5 is made of an LSTM layer with an input size of 256 and a 128-cell hidden layer with a dropout probability of 0.5. Layer 6 is made of two fully connected (FC) layers and a ReLU activation function hidden layer to determine the output of the nodes in the FC. The final output layer would have three nodes, corresponding to the three classes of drone, non-drone, and drone and non-drone targets.  
The CNN loss function is denoted as _Loss_ and is calculated as:  
\[Loss(scr,t)=-scr[t]log(\sum_{j}exp(scr[j])), \tag{24}\]  
where \(scr\) is the classification score.  
The context flow of local and global time and space was designed using LSTM, which fuses the local and global spatiotemporal features. The six attributes of the point cloud served as the input independently, and spatiotemporal convolution kernels were applied to extract the spatiotemporal features of the point cloud. However, it is impossible for six distinct drone and non-drone target identification to adequately capture intrinsic features; hence, these six attributes must be fused. As a result, the fusion network is developed in Layer 4 to fuse the six features. After fusion, the features are more extensive and may more thoroughly describe a target's dimension, speed, altitude, and 3D spatial position coordinates.