## 5 Results
### Results of Distinction  
After determining the target position, a novel feature of HHW was extracted to identify the human target from the construction machinery. A total of 10 groups of radar data were collected from the same construction machinery and human target. The measurement interval of each data acquisition was more than 5 min, and the positions of the human changed slightly within the range of 2-4 m each time. Moreover, the continuous digging action of construction machinery can change to some degree each data acquisition. Therefore, the states of the targets and the environmental interference were not identical for each data acquisition. Thus, the 10 groups of radar data can be regarded to arise from different datasets. The HHW values of the 10 groups of radar data for humans and the digging construction machinery positions are listed in Table 2.  
Apparently, the HHW values of the digging construction machinery are much larger than those of the human target. To describe more accurately this type of difference, a line diagram was used to compare the HHW values, as illustrated in Figure 9. The HHW values of humans are less than those of the digging construction machinery. There are large value differences between them with no intersections. Statistical parameters including mean, standard deviation, skewness, and kurtosis [32] are employed to quantitatively describe these kinds of differences, as shown in Table 3. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & \multicolumn{6}{c}{**HHW Values**} \\ \hline Human & 11 & 13 & 11 & 10 & 9 & 4 & 5 & 8 & 14 & 13 \\ Digging construction & 27 & 19 & 19 & 23 & 18 & 34 & 38 & 26 & 32 & 27 \\ machinery & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Half-height width values of 10 groups of human radar data and the digging construction machinery positions. Figure 8: Results of wavelet entropy decomposition and the ICMLD-CFAR. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**HHW Values** & **Mean** & **Standard** & **Skewness** & **Kurtosis** \\ \hline Human & 9.8 & 3.1875 & 2.1297 & \(-\)0.5217 \\ Digging construction & 26.3 & 6.4506 & 1.9533 & 0.3198 \\ machinery & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical parameters for quantitatively describing differences between HHW values of human and digging construction machinery. There is significant difference in the mean and standard values, more than double. The skewness of HHW values of human and digging construction machinery are similar, both reflecting a positive skew distribution. The kurtosis of human HHW values is less than zero, denoting that the distribution of human HHW values is high and sharp compared with the normal distribution, whereas the kurtosis of the HHW values of digging construction machinery is larger than zero, denoting a short and fat data distribution. Then, Levene's Test was implemented to check for variance equality, and the corresponding \(p\)-value was 0.0667 \(>\) 0.05. Thus, the HHW values of human and digging construction machinery satisfy variance equality. Subsequently, a two-independent sample \(t\)-test was applied to the above two groups of HHW values, and the calculated \(p\)-value of the \(t\)-test was \(1.9\times 10^{-6}\)\(<<\) 0.05. Thus, there is a statistical difference between the HHW values of human and digging construction machinery. The above experimental results show that the proposed method can effectively distinguish human targets from working construction machineries. Traditional methods used for the detection of targets are usually based on finding the range point with maximum energy [31], as shown in Figure 10.  
However, it is not suitable in this type of postdisaster rescue situation because there could be many strong interferences or noises whose vibration energies are much larger than those of humans, such as the walking of rescuers or the mechanical vibrations when clearing up the ruins for reconstructing roads.