## 4 Results and Comparison  
Among the simulations and experiments, 30 signals were obtained, each one processed with VCM to calculate the corresponding CNRs of a metallic object. After this, FSV provided us with a numerical value for the comparison between the reconstruction and the input signal, as shown in Table 1. The execution time in MATLAB 2021a using a core i7 computer  
Figure 11: Backscattering signal reconstruction with the Cauchy and VCM methods corresponding to an electric metal detonator for IEDs in Colombia. Figure 12: CNRs calculated with both the Cauchy and VCM methods using the frequency response of an electric metal detonator for IEDs in Colombia. with 12 GB RAM and the number of samples used are also shown in the results for the simulation setups in Table 2. As the signal became more complex, the CNR extraction method reduced its accuracy. This means that the resonances of the object were less valid for postprocessing and analysis. The average FSV for the simulated signals was 0.021, which classifies the reconstruction with VCM in these scenarios as _Excellent_. For the experimental signals, the results are shown in Table 3, where the average FSV result was 0.1989 being classified in the _Very Good_ category. Now, although the validation in the simulated and experimental scenarios resulted in a _Very Good_ reconstruction on average, we also compared the VCM results against the Cauchy method, which is the most updated frequency domain CNR calculation method used in GPR. The average FSV result for the Cauchy method was 0.161, while for VCM it was 0.128. Furthermore, in Figure 13, the values of the FSV result for each scenario are shown. \begin{table}
\begin{tabular}{l l} \hline \hline
**FSV Categories for Result Value \(\mathbf{v}\)** \\ \hline Excellent & _vs._\(<=0.1\) \\ \hline Very good & \(0.1<\textit{vs.}<=0.2\) \\ \hline Good & \(0.2<\textit{vs.}<=0.4\) \\ \hline Fair & \(0.4<\textit{vs.}<=0.8\) \\ \hline Poor & \(0.8<\textit{vs.}<=1.6\) \\ \hline Extremely poor & \(\textit{v}>1.6\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Categories for the similarity of the two signals according to the FSV results. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **FSV** & **\# of Samples** &
\begin{tabular}{c} **Execution Time** \\ **(ms)** \\ \end{tabular} \\ \hline \multirow{5}{*}{**Thin-Wire**} & 0.001 & 37 & 98.5 \\ \cline{2-4}  & 0.001 & 37 & 99.7 \\ \cline{2-4}  & 0.002 & 37 & 118.9 \\ \cline{2-4}  & 0.063 & 21 & 95.3 \\ \cline{2-4}  & 0.041 & 21 & 95.7 \\ \cline{2-4}  & 0.014 & 21 & 92 \\ \cline{2-4}  & 0.0001 & 21 & 95.8 \\ \cline{2-4}  & 0.0002 & 21 & 94.8 \\ \cline{2-4}  & 0.0004 & 21 & 97.3 \\ \hline \multirow{5}{*}{**Sphere**} & 0.02 & 37 & 102 \\ \cline{2-4}  & 0.077 & 37 & 122.1 \\ \cline{2-4}  & 0.037 & 37 & 105.6 \\ \cline{2-4}  & **Average** & **0.021** & **29** & **101** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulated scenarios’ FSV results for VCM, with execution time and the number of samples used. Here, the Cauchy method outperformed in execution time for all backscattered signals with an average of 61 ms, while for VCM, it was 103 ms. In addition, the reconstruction accuracy for the simulated scenarios was also better in the Cauchy method by 0.0116 in the FSV factor. For all experimental setups, the VCM's FSV factor was always below Cauchy's, having a more accurate reconstruction; hence, its CNRs' extracted were more reliable in these scenarios by 24.2%. As the accuracy of the reconstructed signal, i.e., the accuracy of the CNRs calculated with previous frequency domain methods is considerably affected by noise, the