## 4 Integration of Multi-Sensory Information for Target Identification
### Results of Classification Using Vibration, Acoustic and Radar Data  
In this section, results of classification are presented using vibration, acoustic and radar data. We process power spectral densities of sensor data obtained by MUSIC algorithm using normalization and Principal Component (PC) filters in Weka software package [14]. We compared three classifiers: Random Forest, MLP and SVM. Weka produces a number of metrics for evaluating the performance of the classifiers used. The \"Correctly Classified\" metric is self-explanatory. The ROC curve is a plot \begin{table}
\begin{tabular}{|c|c|} \hline
**Velocity (m/s)** & **Range (m)** \\ \hline
0.65 & 2.32 \\
0.75 & 1.90 \\
0.75 & 1.79 \\
0.75 & 1.67 \\ \hline \end{tabular}
\end{table} 
Table 5: Range and velocity estimation during tracking experiment. of the true positive rate against the false positive rate for the different possible cut points of a diagnostic test. It shows the tradeoff between sensitivity and specificity. The area under the curve (ROC area) is a measure of test accuracy. Kappa statistics indicate whether predictions and actual classes are correlated. In Table 6, the performance of the acceleration (vibration) sensor is presented. From Table 6 it is seen that for all three experiments the classifiers were able to distinguish with good accuracy between different trains, different speeds, as well as between single train or a train with multiple railcars. This conclusion is true with the exception of SVM for the multiple cars experiment, where only 60% classification accuracy is obtained with SVM. The observed degradation of the performance is most likely due to the small data set being used (40 experiments). The ROC Area shows that in general there is high test accuracy for vibration data. Kappa statistic shows that the predictions provided by vibration data are stable, with the exception of SVM for multiple cart experiments. We present the results of acoustic classification in Table 7. Two observations can be made from this Table. First, we can successfully classify two different trains when they are moving, as the trains create different acoustic signals. Second, we can also classify different speeds of the moving trains. The ROC Area and Kappa Statistics also support these findings. Vibration or acoustic data do not allow us to differentiate among different reflectors, as those signals are not influenced by electromagnetic properties of the target. For that classification, we can \begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline \multirow{2}{*}{**Classes**} & **Number** & \multirow{2}{*}{**Classifier**} & **Correctly** & **ROC** & **Kappa** \\  & **of Runs** & & **Classified (\%)** & **Area** & **Statistics** \\ \hline Train A & & Random Forest & 92.5 & 0.99 & 0.85 \\ Train B & 240 & MLP & 97.5 & 1.00 & 0.95 \\
[0.56 m/s] & & SVM & 93.6 & 0.93 & 0.96 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Three \\ velocities \\ \end{tabular} } & \multirow{3}{*}{1,200} & Random Forest & 91.6 & 0.98 & 0.88 \\  & & MLP & 87.6 & 0.96 & 0.82 \\  & & SVM & 92.6 & 0.94 & 0.89 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Multiple \\ Railcars \\ \end{tabular} } & \multirow{3}{*}{40} & Random Forest & 92.5 & 0.98 & 0.85 \\  & & MLP & 97.5 & 0.98 & 0.95 \\ \cline{1-1}  & & SVM & 60.0 & 0.60 & 0.20 \\ \hline \end{tabular}
\end{table} 
Table 6: Classification results using vibration data.