# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
## 4 Results
### Comparative Analysis of Radar-Measured Radial Velocity
Therefore, this study optimizes traditional validation assessment methods by using the minimum deviation between the measured and reference values of the UAV velocity as the evaluation criterion for optimal absolute accuracy. It can effectively reduce the inspection error introduced by the system deviation of the UAV. At the same time, even when the UAV was in a relatively unstable high-velocity state, the relative accuracy of the radar radial velocity validation remained less than 0.2. Furthermore, the optimum absolute accuracy of synchronous comparisons between the UAV and radar was no greater than 0.05 m/s. This conclusively demonstrates that weather radar radial velocity validation based on the RTK UAV can quantitatively reflect the radar velocity measurement capabilities and that the method has good robustness. Based on Figure 14b,d, it is evident that as the velocity increases, the UAV intensity and velocity values measured by the radar become more stable and smoother. Conversely, at lower velocities (Figure 14a,c), the fluctuations in these values become more pronounced. Based on the simulation of the target intensity using the sinc function, as shown in Figure 15, it can be observed that a slower velocity corresponds to a longer duration at the same distance. There is also an increase in the number of sampled points involved in the weighted average. The amplitude variation of the sampled points becomes relatively larger, leading to more pronounced fluctuations in the processed actual intensity values. Conversely, if the target velocity is higher, resulting in a shorter duration, the number of sampled points involved in the weighted average is reduced. As a result, the processed actual intensity values have relatively smoother characteristics. This empirical pattern is consistent with the sampling principles inherent in radar signal processing for target analysis. Figure 14: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Preset** & **Range** & **Maxima** & **Maxima** & **Radar V** & **UAV** & **Difference** & **Preset V1** & **Range (m)** & **Maxima** & **Radar V** & **UAV** & **Difference** \\ \hline \multirow{8}{*}{\(+7\)} & \multirow{8}{*}{825} & 47.11 & 6.98 & 7.00 & 0.02 & & & 57.59 & \(-\)9.95 & 9.80 & 0.15 \\  & & 47.47 & 6.94 & 7.00 & 0.06 & & & 57.86 & \(-\)9.95 & 9.80 & 0.15 \\  & & 47.97 & 6.98 & 7.00 & 0.02 & & & 57.95 & \(-\)9.95 & 9.80 & 0.15 \\  & & 47.75 & 6.92 & 7.00 & 0.08 & & 57.84 & \(-\)9.95 & 9.80 & 0.15 \\  & & 47.79 & 6.98 & 7.00 & 0.02 & & & 58.15 & \(-\)9.95 & 9.80 & 0.15 \\ \cline{2-10}  & & 47.38 & 7.03 & 7.00 & 0.03 & & & 58.59 & \(-\)9.97 & 0.02 \\  & & 47.47 & 6.9 & 7.00 & 0.10 & & 58.84 & \(-\)9.98 & 9.97 & 0.01 \\  & & 47.54 & 6.96 & 7.00 & 0.04 & & 58.95 & \(-\)9.95 & 9.97 & 0.02 \\  & & 47.86 & 6.96 & 7.00 & 0.04 & & 58.9 & \(-\)9.95 & 9.97 & 0.02 \\  & & 47.11 & 6.92 & 7.00 & 0.08 & & 59.02 & \(-\)9.95 & 9.97 & 0.02 \\ \cline{2-10}  & & 51.86 & 6.96 & 7.00 & 0.04 & & & 59.2 & \(-\)9.98 & 9.50 & 0.48 \\  & & 52.13 & 6.94 & 7.00 & 0.06 & & 59.47 & \(-\)9.98 & 9.50 & 0.48 \\  & & 51.83 & 6.96 & 7.00 & 0.04 & & 59.33 & \(-\)9.95 & 9.50 & 0.45 \\  & & 51.29 & 6.96 & 7.00 & 0.04 & & 59.27 & \(-\)9.94 & 10.05 & 0.11 \\  & & 51.47 & 7 & 7.00 & 0.00 & & & 59.09 & \(-\)9.94 & 10.05 & 0.11 \\ \hline \multirow{2}{*}{\( \begin{array}{c}\text{Deviation}\\ \text{Weight}\\ \text{Minimum}\\ \end{array} \)} & \begin{tabular}{c} Maximum \\ 0.02 \\ \end{tabular}  & \ \begin{tabular}{c} 0.04 \\ \end{tabular}  & \begin{tabular}{c} Relative \\ Accuracy \\ \end{tabular}