# Small-UAV Radar Imaging System Performance with GPS and CDGPS Based Motion Compensation
## 4 Discussion  
The presented results represent a limited number of cases which allow at corroborating some general observations about the reconstruction capabilities of the imaging strategy performed in sounder modality but they do not provide an exhaustive analysis. A first obvious remark is that targets are detectable if their backscattered signals collected by the radar are distinguishable from clutter and noise. Hence, whatever UAV positioning technology is adopted, the correct number of targets is expected to be identified in the tomographic image even if, depending on their radar cross section, some targets are more clearly visible than other ones. The second observation is that, as expected, in general the CDGPS positioning data allow better imaging capabilities than standalone GPS data and they made possible to estimate the horizontal distance occurring between targets as well as the target elevation from the ground with a reduced amount of error. This happens even if the achieved CDGPS accuracy was not the same for all the considered examples as it is confirmed by Table 7. This latter reports the percentage of fix/float solution, the number of visible satellites, the average values of Geometric Dilution Of Precision (GDOP), Positional DOP (PDOP), Horizontal DOP (HDOP) Vertical DOP (VDOP), and the mean East, North, and Up Standard Deviations. In other words, even in float mode (estimated positioning uncertainty of several centimeters), CDGPS is shown to effectively support radar imaging. On the other hand, it is sometime possible that GPS-based motion compensation provides decent radar imaging performance and this happens when space-time correlation of positioning errors is significant. Specifically, the imaging degradation experienced by using GPS occurs when positioning data are affected by drifts, while biases play a less significant role. These results are explained by taking into account the relationship between data and unknowns of the imaging problem, see Equation (1). The kernel of this relationship depends on the knowledge of the relative distance between the UAV radar system and the imaging domain; hence, as more precise is the knowledge of this relative distance as more accurate the imaging results are. In addition, it is worth pointing out that the imaging domain is defined according to the available positioning information. As a consequence, while a constant and unknown bias is detrimental for the absolute localization of the targets, it does not affect the imaging capabilities of the imaging strategy. The bias affecting the generic measurement point \(\mathbf{r_{m}}\) occurs also in definition of the generic point \(\mathbf{r}\) of the investigated domain and thus it is erased by computing their distance. The final remark is about the computational time. As said in the Section 2, the MoCo allows the use of the same scattering operator for the Zoom and Shift implementation of the TSVD based inversion strategy. Therefore, the SVD computation is performed in one single shot and it is used for all the subdomains. The computational time required to obtain the tomographic images are given in Table 8 and are referred to the use of a modern laptop whose main hardware and software characteristics are:  
* Processor: Intel\({}^{\text{\textregistered}}\) Core\({}^{\text{TM}}\) i7- 4510U CPU @ 2.00 (GHz)-2.60 (GHz);
* RAM: 8.00 (GB);
* Operative System: Windows 10 Pro