# Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar
## 3 Target Classification in Passive Forward Scattering Radar
### Evaluation of Target's Baseline Crossing  
The clustering of the target baseline crossing, \(R_{R}\) (distance between vehicle to the receiver) in passive FSR can be predicted by using the same system as shown in Figure 8. The effect of vehicle's speed to the overall precision is also analyzed. The variance explained from the output of PCA is \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Compact** & **Saloon** & **Small SUV** \\ \hline Actual car used during & & & \\ experiment to represent & & & \\ each category & & & \\ \hline No. of collected data & & & \\ used for classification & & & \\ \hline Vehicle’s Dimension & \(L=3395\) mm & \(L=4360\) mm & \(L=4420\) mm \\ \cline{2-4}  & \(H=1415\) mm & \(H=1385\) mm & \(H=1740\) mm \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Selected vehicle’s detail for each category used for classification system in passive FSR. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Vehicle-Category**} & \multirow{2}{*}{**NO.V–For Testing**} & \multicolumn{3}{c}{**Automatically Classified as (\%)**} \\ \cline{3-5}  & & **Compact** & **Saloon** & **Small SUV** \\ \hline Compact & 30 & **92** & 0 & 8 \\ Saloon & 33 & 0 & **100** & 0 \\ Small SUV & 30 & 4 & 0 & **96** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Vehicle category confusion matrix. Figure 9: (a) Location of training data in PCA space; and (b) the spreading of testing data in the database of PC domain. utilized for the clustering purposes. In statistics, the explained variation measures the proportion of the mathematical model for the variation of a given dataset. Frequently, variation is quantified as variance, where only then the more specific term 'variance explained' can be used [22]. Figure 10 shows the PCA variance explained of the training data for target speeds of 5, 10, 20 and 30 km/h in the PCA space. The results are summarized in Table 4, which highlights the experimental results of the variance explained on the training data. In the experiment, 120 samples were used for each speed of the moving target. Three types of target distance from the passive radar receiver (baseline) were to be recognized by the clustering-based PCA (5, 10 and 20 m). For every type of target's baseline with the same speed, 40 samples were used. Overall, 480 samples were used for the training data in the principal component space expressed by the first two principal components. Finally, the higher speed of the moving target contributes a finer variance explained, such as 82% of the variance of the training data for 30 km/h, which is the highest among the moving target's variation of speed, where it is proficiently used for the first principal component. However, for the speeds of 10 km/h and 20 km/h, the variance explained were 63% and 48% respectively as the PCA was built from components such as the sample covariance, which are not statistically robust. This meant that the PCA may be thrown off by outliers and other data pathologies. How seriously this affects the results is specific to the data and application [22]. Figure 11a shows the principal component space for a moving target with speed of 5 km/h. It can be seen that the target's baseline of 5 m and 10 m were spread all over the graph. However, the baseline of 20 m was almost isolated in the left area of the PCA space while slightly overlapping with the 5 m and 10 m plots. The spread was due to the slow speed of the ground moving target and it stretched out the time domain shape. Figure 11b shows the training data PCA space for 10 km/h, whereby we can almost ascertain that the target's baseline has its own regions. The blue stars represent the 5 m range of baseline within the range 0 until 40 of the PC1 and \(-\)40 until 10 of the PC2. Meanwhile, the red triangles of the 10 m range of baseline take over the middle position of graph. Then, the black dots of 20 m range are localized on the upper-left part of the PCA space while slightly overlaps with the 10 m range.