## 4 Data Analysis and Classification
The results shown are the highest percentage achieved by any combination of feature for the defined number of features allowed; the selected features are indicated in the legend. The increase in features used demonstrated diminishing returns or, in fact, reductions in success rates. It was found that higher success rates were achieved  
Figure 11: All individuals Data DA Classifier: Classification accuracy plotted against percentage of training set size used. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Random Forest**} \\ \hline  & \multicolumn{5}{c}{**Training \%**} \\ \hline
**Person** & **20** & **25** & **30** & **35** & **40** \\ \hline A & 74.7 & 74.8 & 73.6 & 75.3 & 75.9 \\ B & 63.4 & 65.4 & 64.1 & 62.7 & 65.4 \\ C & 80.7 & 83.6 & 82.9 & 84.3 & 83.2 \\ D & 87.8 & 93.4 & 92.6 & 91.2 & 92.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Random Forest (RF) Classifier results from each individual person. when using only three of the five potential features, for example, when training with 30% and 40%. This demonstrates that using more features is not necessarily the best way to approach optimizing this classification problem; effort should be placed on ensuring effective features are selected. On average the RF classifier was found to have 17% and 14% greater classification rate in comparison to the linear and quadratic DA classifiers, respectively. This is a significant difference and shows that SVD classification is possible in a TTW scenario, although the absolute level of success rate is far below an effective reliable solution that could be used in real world scenarios. A final test was performed to investigate the effect that training data has on the RF classifier. The previous results show a clear difference in performance based on which individual's actions are being classified. Therefore, analysis has been completed to evaluate how effective a classifier is when only data from a single person is used to train the classifier. The classifier was then tested on input features from the three remaining individuals. The results from this can be seen in Figure 13, where each different marker represent results from a classifier trained only on a specific individual. For this classification, all five features have been used. A significant reduction was found when compared to the five feature result in Figure 12, showing how person dependent the features extracted are. A focus of future analysis should be on isolating features that are as independent of individual as possible to ensure \"blind\" classification can be performed effectively on individuals that have not been used as part of the training process. Figure 12: Data from all individuals: Random Forest Classification accuracy plotted against percentage of training set size used. * (20) [PERSON]; [PERSON]; [PERSON]; [PERSON] Ultra-wideband frequency modulated continuous wave synthetic aperture radar for through-the-wall localization. In Proceedings of the Microwave Conference EuMC 2009, Rome, Italy, 29 September-1 October 2009; pp. 1880-1883. * (21) [PERSON]; [PERSON]; [PERSON], [PERSON]; [PERSON]; [PERSON]; [PERSON] Seeing through walls with a self-injection-locked radar to detect hidden people. In Proceedings of the IEEE Microwave Symposium Digest (MTT), Montreal, QC, Canada, 17-22 June 2012; pp. 1-3. * (22) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Detection of concealed individuals based on their vital signs by using a see-through-wall imaging system with a self-injection-locked radar. _IEEE Trans. Microw. Theory Tech._**2013**, _61_, 696-704. [CrossRef]
* (23) [PERSON] Doppler Signatures of Radar Backscattering from Objects with Micro-Motions. _IET Signal Process._**2008**, \(2\), 291-300. [CrossRef]
* (24) [PERSON]; [PERSON]; [PERSON] _Radar Micro-Doppler Signatures: Processing and Applications;_ Institution of Engineering and Technology: London, UK, 2014. * (25) [PERSON]; [PERSON]; [PERSON]; [PERSON] Micro-Doppler Effect in Radar: Phenomenon, Model, and Simulation Study. _IEEE Trans. Aerosp.