## 3 Results and Discussion
### The Synthetic Data Generation  
The reasons for synthetic data generation are  
* The comparison of the simulation output with the experimental measurement results;
* The validation of the developed EM wave propagation model;
* The generation of data for the training of the NN and development of NN architectures. The data generation at different stages of project was performed using different signals and different scene setups, as described below. _Simulated Scenes_ Two types of scenes were simulated:
* The dielectric block wall scene was simulated imitating monostatic and bistatic radar configurations in order to
* Validate the developed EM wave propagation model;
* Compare synthetic data with experimental measurements;
* Generate data sets for NN training and evaluation.  
Simulation of this scene took a lot of time since the dimensions of the scene were around 70 wavelengths, and real radar antennas in the simulation were used, which lead to a very large mesh size. The front of the wall was up to 4.4 \(\times\) 4.4 m (in order to shift the reflection from the wall vertexes outside the time window when the signal is reflected from the bricks of the back wall); the physical parameters of the scene were variable:  
* Distance to the wall, 350 to 700 mm;
* Thickness of the wall, 200 to 500 mm;
* Relative dielectric permittivity of the wall, 1 to 6;
* Dielectric losses of the wall, 0 to 0.1.  
Figure 4: Three-layer model with an object representing one layer of dielectric medium.  
- Relative dielectric permittivity of the ball, 1 to 6;
- The Gaussian signal (Figure 5a), since it is continuous and its spectrum due to the Fourier transform properties is also Gaussian, and therefore also continuous. The spectrum of the used Gaussian signal is similar to the spectrum of the radar signal used at the beginning of the project, so this signal was used during simulations of the first data sets for NN training, since the model of the developed EDI radar was not yet developed;
* The sine signal truncated to a single period (Figure 5b), since it provided better results in terms of finding the proper positions in time when analyzing the reflected signals in the developed model of EM wave propagation;
* The signal of the EDI radar (Figure 5c), since it should provide simulation results that were the most similar to the experimental results. The signal of the EDI radar was modeled using the 66 most powerful components of the discrete cosine transform from the recorded EDI radar signal. This signal was used for the generation of synthetic data for NN training and evaluation. This signal was also used for noisy transmitted signal generation, varying, by up to 20%, the DCT coefficient values in order to simulate the imperfectly matching radar signals. Additional noise on demand was added to the received signals in order to represent noisy signals received by the radar. Spectra of the signals are shown in Figure 6: as can be seen, the signal of the EDI radar is close to perfect for measurements, since the variation in power is less than \(\pm\)1.3 dB in the 0.5 to 5 GHz band. [FIGURE  
Figure 5: Normalized signals used in simulations: (a) blue line—Gaussian signal _SetGaussExcite(FDTD, 1e9, 2.5e9)_; (b) red line—truncated sine signal; (c) green line—Signal of EDI radar.