## 4 Results
Similar results occurred when adding the noise to the EIT measurements, which can be seen in Figure 13. Thus, the benefits occur also when noise is present. The graphs of GREIT figures of merit are qualitatively similar to Figure 11; however, when 0.75% noise is added, increasing \(R_{2}\) at first does not give additional spatial information, as all the performance metrics stay in a plateau until around \(R_{2}=4\). Subsequently, the performance metrics have the same behavior as in the no-noise case. We next analyzed the behavior of wrong conductivity estimations in the radar _a priori_ information. While the conductivity estimate of the _a priori_ radar data for the previous plots was taken from the literature, we altered the conductivity estimate for the radar. The estimated conductivity from the radar measurements were scaled by \(\gamma:=\{0.001,\ 0.01,\ 0.1,\ 1,\ 10\}\). \(R_{1}\) was set to 3 and \(R_{2}=4\), while no noise was used. Figure 9:0 GREIT figures of merit for the combination of EIT and radar data, for \(R_{1}=3\) with 0.75% noise. The orange line represents the scores of the EIT-only data. \(R_{2}\) was increased from 3.25 to 5.5 in steps of 0.25. The results can be seen in Figure 9:1 **AR, PE, RES**, and **SD** have the same qualitative behavior. They are on a plateau from 0.001 to 0.01 and start to decrease for \(\lambda\) from 0.01 to 1. From 1 to 100, the values are building a plateau and increasing only slightly. For practical use, [PERSON] et al. developed a nonlinear estimation algorithm for the estimation of an a priori conductivity [37]. When fixing \(R_{1}\) and increasing \(R_{2}\), we could observe that, at first, the results tended to be better, but after \(R_{2}=4.5\), the figures of merit tended to become worse again in some figures and a little bit better in others. At first, this behavior seems strange; however, the radar information is only available along the x-axis. Thus, the more radar information is added, the more the reconstruction tends to be narrowed down along the x-axis, but, at the same time, is expanded along the y-axis. This results in an increase in the **PE** as the center of gravity of the quarter-amplitude set is drawn to the center of the y-axis. The same holds true for the **SD**. The behavior of the **AR** and **RES**, however, will become better as more radar information is added. This can still be explained. As the radar information contains solely values from the k-space with \(R_{1}<|k|\), only the fine-detail components are  
Figure 9:2 GREIT figures of merit for the combination of EIT and radar data, for \(R_{1}=3\) without noise and \(R_{2}=4\). The radar estimation conductivity was changed and scaled by \(\gamma:=\{0.001,\ 0.01,\ 0.1,\ 1,\ 10\}\). The x-axis is scaled logarithmically. enteringinto the reconstructed image. Thus, the area of the reconstruction tends to decrease, which results in an increase in the aforementioned metrics; however, the best value for \(R_{2}\) in our setup lies around 4.0. The conductivity for the calculation of the scattering transform needs to be estimated because our radar setup only provides spatial information. In the previous case, we took the exact value of simulated conductivity. This, however, is not a valid estimation for the real-world case. We simulated different conductivity estimates to check the stability. Since the simulated enclosure has a higher value than the background conductivity, overestimating the conductivity tends to lead to slightly better results, which are barely observable in the reconstruction (see Figure 11). However, the improvements are very minor. This might no longer be the case for more complicated geometries. Underestimation of the conductivity, however, leads to a strong decrease in reconstruction quality, as low-frequency spatial information from EIT and high-frequency spatial information from radar in the k-space compete against one another during the reconstruction.