## 5 Conclusions and Future Work  
In this work we have developed an experimental methodology for a distributed multi-modal system for surveillance and tracking using radar and supplementary sensor data. Our research goal is to develop a prototype sensor network using commercial off-the-shelf components and to provide innovative solutions for detecting, tracking and classifying different targets and events under conditions with high noise and clutter [15]. Our distributed system supports a pervasive sensor network approach and it consists of a suite of radar transceivers, supplemented with acoustic, vibration, and light sensors. Note the results presented in this work show the basic capabilities of different sensor modalities in an integrated wireless network platform. The experiments with our low-cost sensors show the feasibility of detecting, tracking and classifying different types of targets. More specifically, with the integrated sensor network we are able to differentiate between the same model trains mounted with different reflectors (radar), two different model trains (vibration and acoustic), as well as the same model train with different loads (vibration). The major results of our studies are summarized as follows: \begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline \multirow{2}{*}{**Profile**} & **Number** & \multirow{2}{*}{**Classifier**} & **Correctly** & **ROC** & **Kappa** \\  & **of Runs** & & **Classified (\%)** & **Area** & **Statistics** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Tetrahedron: \\ Metal—Paper \\ \end{tabular} } & \multirow{3}{*}{640} & Random Forest & 85.2 & 0.91 & 0.70 \\  & & MLP & 87.7 & 0.93 & 0.75 \\  & & SVM & 84.1 & 0.84 & 0.68 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Rectangular: \\ Metal—Plastic \\ \end{tabular} } & \multirow{3}{*}{640} & Random Forest & 80.1 & 0.89 & 0.60 \\  & & MLP & 81.1 & 0.87 & 0.62 \\  & & SVM & 80.0 & 0.80 & 0.60 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Metal: \\ Tetrahedron— \\ Rectangular \\ \end{tabular} } & \multirow{3}{*}{640} & Random Forest & 74.2 & 0.82 & 0.50 \\  & & MLP & 82.2 & 0.86 & 0.64 \\ \hline \end{tabular}
\end{table} 
Table 8: Classification results using radar data. 1. The introduced wireless radar sensor detects the range and velocity of targets with good precision, as confirmed by cross-validation measurements. 2. Radar transceivers, in conjunction with vibration and acoustic sensors, can be used effectively to classify different types of objects. The radar can classify various types of material properties and shapes, while the acoustic and vibration sensors can differentiate among objects of different weight and movement characteristics. Acoustic and vibration sensors can indicate an approaching target even when it is not within the field of view of the radar or optical sensors. 3. Integration of radar sensors with a range of wireless sensor motes is an innovative step toward building robust decision support based on a pervasive sensor network for surveillance and tracking in various practical scenarios. It is important future task to explore scenarios when the multi-modal aspects of the sensor system is properly harnessed [16]. In order to improve the portability and autonomy of our system, we are currently testing our radar-motes with on-board power and signal generation chips. In future research, we plan to experiment with more complex radar tasks and advance the described sensor fusion and target identification, as well as we will investigate the problem of tracking multiple objects. This research has been supported in part by a research grant of the FedEx Institute of Technology, the University of Memphis. The work by one of the authors ([PERSON]) has been supported by the Air Force Office of Scientific Research. (AFOSR/NL), [PERSON] and [PERSON]. [PERSON], US Air Force Research Laboratory, Sensors Directorate, Hanscom AFB, MA, and [PERSON], Solid State Inc., Nashua, NH have contributed significantly to the research reported here.