## 4 Results  
Firstly, we gave an example of an EIT reconstruction in our squared domain. We used the D-Bar algorithm described in the methods to obtain the reconstructions. The reconstruction can be observed in Figure 8, in the bottom left. The EIT reconstruction of the target is bigger than the ground truth. Throughout the whole paper, the truncation radius was chosen empirically, in order to better see the effects. To also show the performance of the reconstruction on two targets, we added, in Figure 9, a plot of two targets with the settings. It showed the same characteristic of better resolution along the x-axis. The values were in the range typically reported by the literature [13; 22]. Figure 8: Top: Ground truth of the conductivity enclosure. Bottom left: EIT reconstruction with no noise added and \(R_{1}=3\). Note: There is no _a priori_ information in the image. Bottom right: Combined reconstruction with _a priori_ information with a truncation radius \(R_{2}=4\). Figure 9: Top: Ground truth of the two conductivity enclosures. Bottom left: EIT reconstruction with no noise added and \(R_{1}=3\). Note: There is no _a priori_ information in the image. Bottom right: Combined reconstruction with _a priori_ information with a truncation radius \(R_{2}=4\). As a second reconstruction example, we tested an EIT reconstruction with \(R_{1}=3\), but added noise of 0.75%. The ground truth, the EIT reconstruction, and the combination with radar data are shown in Figure 10. Compared to Figure 8, the EIT reconstruction is more blurred, while the combined reconstruction is thinner along the x-axis. As a third example, we gave two reconstructions with _a priori_ information and two different estimations of the conductivity in the _a priori_ data. The results are shown in Figure 11. The underestimation (scale factor of 0.001) resulted in a reconstruction similar to that of Figure 8, while the overestimation is similar to the combined reconstruction in Figure 10.  
Figure 11: Visualization of under- and overestimation of the gourd truth conductivity. Top: Ground truth of the conductivity enclosure. Bottom left: Combination reconstruction with \(R_{1}=3\) and \(R_{2}=4\). The original conductivity of the enclosure is underestimated by a factor of 0.001. Bottom right: Combination reconstruction with \(R_{1}=3\) and \(R_{2}=4\). The original conductivity of the enclosure is overestimated by a factor of 100. Figure 10: Top: Ground truth of the conductivity enclosure. Bottom left: EIT reconstruction with 0.75% noise added and \(R_{1}=3\). Bottom right: Combination of noisy EIT data (0.75% noise) and radar data. \(R_{1}=3\) and \(R_{2}=4\). The noise was added to the voltages of the electrodes in percentage of the peak-to-peak value. Regarding the analysis with the GREIT figures of merit, we tested the EIT and radar reconstruction for an EIT truncation radius of \(R_{1}=3\) without noise in Figure 12. **AR** became lower with an increasing radar truncation radius \(R_{2}\), and reached a plateau for \(R_{2}=4.75\). It then decreased again for \(R_{2}=5.5\). The **AR** with radar information was lower than without. **PE** followed another pattern: it decreased until \(R_{2}=4.25\) was reached and increased after that. The trend for **RES** followed the same behavior as **AR**. It first decreased until \(R_{2}=4.75\). After that, it reached a plateau, but the value was still lower than without radar information. It increased again at \(R_{2}=5.5\). **SD** decreased with increasing truncation radius \(R_{2}\). Instead of a plateau after \(R_{2}=4.7\), the value increased again and surpassed the pure EIT reconstruction. **RNG** behaved differently; instead of decreasing, it increased steadily until it reached a plateau at \(R_{2}=4\); after that, it decreased again. Figure 12: GREIT figures of merit for the combination of EIT and radar data, for \(R_{1}=3\) without 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.