# Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar
## 3 Target Classification in Passive Forward Scattering Radar  
The different categories of vehicle used for the classification is shown in Table 2. This paper chose three vehicle categories to be analyzed. These categories are assumed to be the most suited representation of the typical vehicle's type on the road. In addition, if the system can classify these three vehicle categories which have close proximity in size between them, it could then be much easier to classify for vehicle with huge different in size between them (for example lorry and motorbike). During all the testing campaign, the same vehicles as in Table 2 were used to represent the type of category, even at different day and time of experimental campaigns. Figure 8 illustrates the classification block diagram used in the proposed passive FSR system. It is divided into two sub-systems: the signal pre-processing that analyzes the input time-domain signature including signal normalization. The second sub-system consists of the classification itself, which includes features extraction, training and testing. In the pre-processing block, the time domain signature is first de-noised using wavelet to smooth the signal. Then, the Power Spectral Density (PSD) for each vehicle is then estimated. It was calculated by Welch algorithm, which is utilized to estimate the power of a signal at different frequencies based on the concept using a periodogram [21]. The PSD is used as the input to the classifier due to the Doppler frequency that contain within the scattered signal. The amplitude for each Doppler frequency follows the radiation pattern of aperture antenna with shapes of vehicle's silhouette. Thus, the PSD shape is unique to the vehicle's shape. The next step in the classification system is the amplitude and speed normalization. The amplitude was normalized to the maximum value and speed was normalized based on the relationship of \(\Delta f=v/L\), where \(\Delta f\) is the FS first null (main lobe width) in the spectrum, where \(v\) is the speed of vehicle, and \(L\) is the maximum length of the vehicle [2]. However, the data of spectral signature in PSD is too large, hence Principle Components Analysis (PCA) is used to represent the PSD signature in more valuable information. The data representation in PCA domain is then used for the classification process. The high dimensionality and possibly, highly correlated features from the PSD data is reduced by the PCA. Although PCA will reduce the dimensionality and number of data by exploiting the correlation between the features in PSD, in reality the output will inherently cluster the data to its category. Hence, the output data in the PCA space is used as training and testing vectors prior to the classification algorithm.The paper employed the established KNN algorithm as classifier in the system. The PCA technique converts high-dimensional data onto a lower-dimensional in a principal component (PC) space. The PCs are arranged so that the amount of variance of the information by each PC is non-increasing. Thus, the first PC carries the highest information of variance in the data and  
Figure 7: Received scattered signal in **(i)** time domain; **(ii)** power spectrum and **(iii)** spectrogram of moving vehicles crossing the Tx-Rx radar baseline for Compact and Saloon. so on. In the PCA algorithm, the input feature vector, \(\gamma\) is transform linearly into the new PC space as represented by Y in the equation below [2; 20]. \[Y=H\cdot\left(\gamma-g\right)^{T} \tag{10}\]  
where \(g\) is mean feature vector of the training data and \(H\) is a transformation matrix obtained from the training data. If the covariance matrix that was calculated from total training features set is denoted by S, then, the PCA decomposes the covariance matrix S into S = ULU\({}^{\text{T}}\). Where L and U is a diagonal N \(\times\) N matrix of a non-increasing order of the Eigen values and Eigen vectors respectively.