## 3 Results and Discussion
### Parameter Estimation Using the NN
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Repres.** & **Signal** & \begin{tabular}{c} **Error in** \\ **Prediction \(\times\) 10\({}^{-3}\)** \\ \end{tabular} & \begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} & **Parameters** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Time \\ domain \\ \end{tabular} } & Refl. & \(170.37\pm 4.81\) & 9.12 & 13625 \\  & Trans. & \(195.12\pm 3.99\) & 8.96 & 13625 \\  & Bist. & \(123.39\pm 2.67\) & 19.79 & 25913 \\  & Multist. & \(60.61\pm 1.96\) & 24.83 & 38201 \\ \hline \multirow{4}{*}{PCA} & Refl. & \(2.82\pm 0.08\) & 2.8 & 1619 \\  & Trans. & \(3.25\pm 0.09\) & 2.98 & 1619 \\  & Bist. & \(2.59\pm 0.05\) & 2.86 & 1619 \\  & Multist. & \(1.77\pm 0.03\) & 3.39 & 1619 \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Amplitude \\ spectrum \\ \end{tabular} } & Refl. & \(107.77\pm 3.41\) & 4.72 & 5385 \\  & Trans. & \(29.65\pm 1.22\) & 5.82 & 5385 \\  & Bist. & \(28.19\pm 0.36\) & 8.91 & 9456 \\  & Multist. & \(24.23\pm 0.23\) & 11.27 & 13569 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Phase \\ spectrum \\ \end{tabular} } & Refl. & \(50.67\pm 2.66\) & 3.86 & 5153 \\  & Trans. & \(19.08\pm 0.38\) & 2.99 & 5153 \\  & Bist. & \(16.37\pm 0.29\) & 4.68 & 9153 \\  & Multist. & \(17.28\pm 0.31\) & 5.99 & 13569 \\ \hline \multirow{4}{*}{STFT} & Refl. & \(12.38\pm 0.21\) & 21.93 & 39569 \\  & Trans. & \(6.43\pm 0.06\) & 26.71 & 39,569 \\ \cline{1-1}  & Bist. & \(6.06\pm 0.05\) & 48.57 & 79,249 \\ \cline{1-1}  & Multist. & \(3.78\pm 0.08\) & 72.02 & 89,285 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relative permittivity \(e_{r}\) parameter estimation with different representations. Finally, the PCA preprocessing showed best prediction error results for both estimated parameters, with \((1.77\pm 0.03)\cdot 10^{-3}\) for \(\epsilon_{r}\) and \((141.03\pm 3.31)\cdot 10^{-6}\) for \(tan\delta\). The performed tests indicate that simplification of the data by feature vector reduction was achieved. Therefore, the training time is reduced (by 21.24 to 38.19 times compared to the STFT on multistatic setup data) along with trainable weight parameters (by 55.15 to 57.0 times). The PCA method also allowed us to train the neural network model with the same number of training parameters for every setup, as the number of principal components remained the same. The method itself was implemented via the _scikit-learn_ machine learning library to perform such dimensional reduction. The PCA class was used to fit and transform the raw input data. _Noise impact on the parameter estimation_  
With the achieved results, the background noise removal, data normalization, and PCA preprocessing enhanced results were achieved for the estimation of the object material and structure. Only transmitted channel signals were used (in the clearly monostatic radar case) to decrease the calculation time. Based on the knowledge gained during the study, transmitted channel (monostatic radar) signals enhanced the parameter value prediction compared to the reflected signal in bistatic radar configuration. Data sets were tested with noise added both to the initial clear synthesized signals and excitation signals of the simulator. Data sets with noise added to the initial clear signals were used to test the NN model and study the impact on the prediction accuracy, with various noise levels added after the data synthesis. The higher the noise level, the harder it is to predict the accuracy of the parameter's value. Training time increases together with the necessary epoch number to reach the prediction error. Noise addition only to parts of the signal has less impact on the prediction accuracy. Even with the SNR of 5 only at the random signal parts, high precision was obtained with only \((69.39\pm 8.9)\cdot 10^{-3}\) and \((1.14\pm 0.01)\cdot 10^{-3}\) average prediction error of the relative permittivity and dielectric loss accordingly. Therefore, the noise addition to parts of the