## 5 Conclusions  
In our study, the existing monostatic radar was upgraded to the multistatic radar with the help of software, firmware, and switches of the antenna signal: different transmission and reception antenna configurations are possible (16:1, 4:1, 1:1, 4:4, 1:4, 1:16). A new optimized antenna was designed for the frequency band 2..10 GHz with the help of free FDTD simulation software OpenEMS [26]. In OpenEMS, simulations generated synthetic data that can be used to study and develop optimized artificial neuron network structures for the estimation of dielectric material properties. The application of the developed mathematical model of EM wave propagation through dielectrics can lead to incorrect estimation of dielectric material properties due to errors in determination of reflected signal position in time and the sensitive equations of the model. Instead, monostatic UWB IR in combination with NN was successfully used for the determination of material properties, such as thickness, dielectric permittivity, and losses, with improved, but limited, accuracy; multistatic radar configuration of such a system increases the estimation accuracy of given properties. As it is often time-consuming and requires expensive equipment, synthetic data gathering was used to reduce the time required for sample collection. Therefore, synthetic data were gathered in small steps to cover the mentioned range of values, with steps of up to 1.0\({}^{-3}\). A non-destructive method was used that allowed us to use the data sets for the CNN to solve the regression problem and estimate one or two varying parameter values, and thus to evaluate the EM properties of the material and internal structure of the objects. With the methodology proposed, the MSE loss function showed a lower prediction error than other loss functions on the single-parameter-varying data set. The two-parameter-varying data sets were introduced with different data representations and preprocessing. Distinct data setups were used to confirm the statement in the paper related to the study project [29]. In almost all cases, the multistatic setup showed the best performance. In the case of the representations, the PCA method and STFT showed favourable performance in parameter value estimation (Tables 2 and 3). While STFT is more robust, PCA enables the reduction in input vector (feature) size that simplifies the NN architecture. This is indicated in Tables 2 and 3. The scale of trainable parameter reduction ranges from 19 to 57 times. The training time reduction ranges from 9.5 to 38 times. The range depends on the setup used during the training process. PCA preprocessing allowed us to reduce training time, improve prediction accuracy, and enhance the possibility of implementing regression problem-solving techniques on a device (microcontroller) or, in other words, on the edge. With noise-added data, the results show that at the noise spikes, data introduction did not affect the prediction capability of the NN model (see Table 4). In comparison, the Gaussian noise of 0.2 level predicted parameters chaotically with differences of 0.63 for the \(\epsilon_{r}\) and \(4.1\cdot 1.0^{-3}\) for the \(tan\delta\) prediction. The 0.2 noise level, therefore, was chosen as the top margin for noise introduction to the generator signal in the simulation environment. With the accuracy achieved by implementing the proposed methodology, data preprocessing, and representations, object material and structure estimations are possible. The NN opens possibilities to extract information about the material structures for further evaluation and comparison of the parameters. It opens possibilities to implement more parameters for evaluation and research on both synthesized and real-life data. It has been proven that in order to speed up the optimal NN model development for real-life applications, it is feasible and useful to develop NNs using synthetic data and to tune NN architecture using real data.