## 3 Results and Discussion
### Parameter Estimation Using the NN  
With the regression problem, the main task for the CNN model's training outcome is to predict the value of the proposed parameter with as little error as possible. The prediction is based on the training of the model on synthesized data signals of various representations. Although the model might converge without feature normalization, it makes training more stable. Furthermore, the background signal was removed from all the signal data (signal with \(\epsilon_{r}=1\) was recorded as air's relative permittivity). Figure 6: Power spectra of normalized simulation signals: (a) blue line—Gaussian signal _SetGaussExcite(EDTD, Te9, 2.5e9)_; (b) red line—truncated sine signal; (c) green line—signal of EDI radar. At first, the regression problem solution was introduced to the neural network with the data sets comprising the single varying parameter to research the impact of different loss functions on the prediction accuracy. The MSE loss function showed the best performance among the other functions; therefore, it was used in further research. The loss function determination was achieved with single-parameter-varying data sets. The improvement in accuracy was established by prediction error decrease with preprocessing actions. Then, the two-parameter-varying data sets were introduced to increase the complexity and to perform further object and material structure estimation, as the \(\epsilon_{r}\) prediction error was as low as (0.12 \(\pm\) 0.01)\(\cdot\)10\({}^{-3}\) with single-parameter-varying data sets. _Setup and data representation impact on the evaluation of parameters_  
Figure 8: The real (red, left axis) and simulated signals (blue, right axis) of developed antenna (Figures 2 and 3) impedance. Figure 7: The simulated (blue) and measured (red) radar signals (shifted due to measurement offset) from a cement brick of thickness 95 mm at a distance 500 mm (correlation 79%). The results achieved for the value prediction from the two-parameter-varying data sets are presented in Tables 2 and 3, with the best performance being achieved by the radar multistatic configuration. The CNN had more information about the signal changes from different perspectives, as the setup used reflected, transmitted, and side signals. Nevertheless, in some cases, one of the signals decreased the performance of the multistatic setup. With the usage of the early stopping call-back function, the model stops training when the learning curve enters the plateau and validation loss is not decreasing. The best training weights were chosen to generate predictions based on the evaluation data set. From the data representation perspective, the worst performance was with the time domain data (filtered raw data). With the data preprocessing, higher prediction precision was achieved:  
* For frequency domain representations, the phase spectra data sets for both estimated parameters showed better results as the data were more informative than in the amplitude spectra data sets. The prediction error is 1.41 and 1.76 times smaller with phase spectrum than the amplitude spectrum for \(e_{r}\) and \(\tan\delta\), respectively (multistatic setup). The spectrum component number impact was also studied, where the latter components served as noise and decreased the performance of the NN training;
* The STFT preprocessing showed improved results compared to the previous data representations due to the ability of the method to extract the most valuable information from time and frequency domain data. With STFT, parameter prediction error is 4.57 units smaller for \(e_{r}\) and 1.07 times smaller for \(tan\delta\) compared to the phase spectrum results for multistatic setup. As the data sets of STFT are presented as 2D images with additional features of RGB color intensity, the number of input data dimensions increased. Therefore, the number of trainable parameters increased by 6.79 to 61.97 times depending on the antenna setup, which led to a training time increase;