# Small-UAV Radar Imaging System Performance with GPS and CDGPS Based Motion Compensation
## 5 Conclusions  
This manuscript has dealt with UAV radar imaging system and a signal processing strategy for generating high resolution radar images in the plane defined by the nadir range and the along-track directions. The signal processing exploits the MoCo procedure based on standalone GPS or CDGPS positioning data, and faces the imaging as an inverse scattering problem. This latter is solved by means of the TSVD inversion scheme, whose implementation has been speeded up by the Shift and Zoom approach. To assess the imaging capability and to evaluate the effect of the UAV positioning data on the reconstruction performance, two measurement campaigns have been carried out in different weather conditions (winter and summer) and by using multiple different targets. The experimental results demonstrate that, in general, inclusion of CDGPS positioning information within the MoCo procedure enables satisfactory imaging results and good target relative localization accuracy, both with fixed and floating solutions. On the other hand, it is sometime possible that standalone GPS-based motion compensation provides suitable radar imaging performance, which happens when space-time correlation of positioning errors is significant. In fact, imaging degradation is associated with the drift of positioning errors during the radar integration time, while biases play a less significant role. This consideration may be important in view of imaging scenarios where CDGPS cannot be used or cannot provide nominal performance levels. Final comments are dedicated to future developments. First of all, it is necessary to perform an investigation involving a large number of field trials in order to perform an in-depth statistical analysis for quantitatively assessing the impact of the positioning techniques on the imaging system capabilities; in this frame, we will exploit merit figures, such as the achievable resolutions and imaging quality parameters as peak contrast and entropy. Then, an imaging sensitivity comparison analysis between the current MoCo imaging approach and 3D UAV positioning integration in the focusing stage will be conducted. Furthermore, an advanced autonomous flight mode with the possibility to plan the UAV flight on a predesigned grid will be accounted for as well as the use of low frequency radar systems allowing underground penetration capabilities. In this frame, imaging procedures devoted to exploit data gathered along multiple lines will be considered in order to obtain a 3D tomographic reconstruction of the investigated scene. In addition, imaging strategies able to account for the different electromagnetic velocity in air and soil will be considered in case of underground penetration. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Flight Test Case** & **TRACK** & **Computational Time (s)** \\ \hline \multirow{2}{*}{Test 1} & Track 1 & 5.16 \\ \cline{2-3}  & Track 2 & 14.15 \\ \hline \multirow{2}{*}{Test 2} & Track 1 & 4.65 \\ \cline{2-3}  & Track 2 & 3.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Computational Time. **Author Contributions:** Conceptualization, C.N., I.C., G.F., and F.S. ; methodology, C.N., I.C., F.S., and G.F.; software and validation, C.N., I.C., G.E., and G.F.; formal analysis, C.N., I.C., F.S. ; investigation, C.N., I.C., G.E., and G.F.; data curation, C.N., I.C., G.E., and G.F.; writing--original draft preparation, C.N., I.C., G.E., A.R., and G.F.; supervision, I.C. ; project administration, I.C. and F.S. ; funding acquisition, I.C. and F.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by CAMPANIA FESR Operational Program 2014-2020, under the VESTA project \"Valorizzazione E Salvaguardia del paTrimonio culturAle attraverso l'utilizzo di tecnologie innovative\" (Enhancement and Preservation of the Cultural Heritage through the use of innovative technologies VESTA), grant number CUP B83D18000320007.