# Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar
## 3 Target Classification in Passive Forward Scattering Radar
The transformation matrix H is then formed by the Eigenvectors corresponding to the first G highest Eigen value, \(H\) = [u1; u2;...; uG]. The overall classification scheme consists of two stages: the training stage and the classification stage. During the training stage, the entire features vector which is based on the PSD value for all the training set is available. These features of the training sets are correspond to each vehicle category. First step in the training stage is to compute the transformation matrix \(H\) from the training data by the PCA algorithm. Each training feature vector is then transformed into the PCA-space by using Equation (10). If Q is to represent the vehicle category, then a set of transformed feature vectors \(\{V^{\text{c}}\}_{i}\) formed for each vehicle category. Feature vectors \(\{V^{\text{c}}\}_{i}\) is saved and will be used as a reference during the classification process. In the classification stage, the passive FSR system will capture an unknown signal from vehicle crossing the baseline. The new signal is converted into the feature vectors set for individual vehicle. By using the same transformation matrix H, it is then transformed to the feature vector \(\mathbf{V}_{j}\) and is placed into the PCA space together with Feature vectors \(\{\mathbf{V}^{\text{c}}\}_{i}\). The final step in classification stage is to apply classifier rule and algorithm to the new signal vehicles' feature vectors, \(\mathbf{V}_{j}\) which is unknown. There are many choices of the established classifier method can be applied. The simplest yet reliable classifier method, which is K Nearest Neighbors (KNN) with Euclidean distance, is used. Here three neighbors were chosen to find the smallest distance before category decision was made by the system. The training data for each category in the first two principal components in the PC space is illustrated in Figure 9a. The figure shows the data for each vehicle category are positioned at different location in the PC space with slight overlap between the categories. Figure 9b shows the plots of testing data on top of the training data. During the experiment, this paper used the first three  
Figure 8: Classification system block diagram in passive FSR using LTE signal. principal components for the classification process, which described 91% of the variance of the training data. The full vehicle's category classification results are presented in Table 3. The results show a high classification performance is obtained with small number of training data. It also shows that the classification system used in passive FSR classifies correctly by 100%, 96% and 92% for Saloon, Small SUV and Compact vehicles, respectively. Although there is slight misclassification by 8% of the Compact as Small SUV, this may be due to the almost similar cross sectional shape between the two categories. The classification system used in the passive FSR system utilize the silhouette shape of the vehicle, which exist in the FS main lobe of the vehicles, therefore the FS main lobe of the Compact would look comparable to the Small SUV except for the different dimensions.