## 3 Results
### Method for Assessing the Reliability of Tracking  
For an objective analysis of the effectiveness of the developed target-tracking method in the radar at the initial stage of operation in conditions of intentional interference, a formalized evaluation methodology is proposed, consisting of the following stages:  
1. Calculation of the smoothed trajectory.  
Using the fusion Formula (8), a vector trajectory \(\hat{x}(t)\) is obtained, which is subsequently used for comparison with reference or 'true' data, if available;  
2. Calculation of efficiency metrics.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Mode** & **Value \(\alpha(t)\)** & **Explanation** \\ \hline Standard & 0.9 & Full Confidence of the Kalman Filter \\ \hline Robust & 0.5 & Balance of the Kalman Filter and the \\  & & GERT Model \\ \hline Predictive & 0.2 &
\begin{tabular}{l} Advantage of the GERT Model at \\ Reduced Confidence \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Variants of the assignment of the weight coefficient \(\alpha(t)\).  
* Root mean square error (RMSE) [26]: \[RMSE=\sqrt{\frac{\sum_{t=1}^{T}\left\|\hat{x}(t)-x_{true}(t)\right\|^{2}}{T}},\] This metric allows to assess the tracking accuracy compared to the real (simulated) trajectory.
* Tracking loss probability [27]: \[P_{lost}=\frac{N_{lost}}{T},\] where \(N_{lost}\) is the number of cycles at which the reliability fell below the acceptable threshold (e.g., \(P_{meas}<0.3\)).
* Average delay in responding to deterioration of conditions [28]: \[D_{response}=\frac{1}{N_{event}}\sum_{i=1}^{N_{event}}(t_{\text{reactions}}^{(i )}-t_{\text{beginningning}}^{(i)}).\]  
This indicator reflects the efficiency of adaptation of the tracking parameters (switching modes, changing \(R\), updating \(\alpha(t)\) after fixing the reliability drop).  
In the simulated environment, a scenario is considered in which the pulsed appearance of interference is observed for 10-20 cycles. The system reacts by decreasing \(\alpha(t)\), increasing \(R(t)\), and shifting confidence towards the GERT model.  
Figure 11 shows a comparison of the reference trajectory \(x_{true}(t)\) with the initial smoothed estimate \(\hat{x}(t)\). It can be seen that during interference, the initial estimate remains close to the true target position. In the control scenario, in which the system does not adapt, the Kalman filter trajectory noticeably deviates from the true one for 3-4 cycles after the loss of measurements. This manifests itself in the form of a 'bounce' of the path and the accumulation of error. In contrast, adaptive fusion with GERT allows us to compensate for these disturbances and maintain more accurate target tracking.  
In the figure above, in the area highlighted in pink, the characteristic deviation of the Kalman filter results caused by the loss of measurements is visible. Due to the lack of updating, the Kalman filter continues to predict based on previous states, which leads to the accumulation of error.  
Instead, adaptive fusion with the GERT model reveals a loss of reliability (due to the low value of \(P_{meas}(t+1)\)), reduces the weight of the Kalman filter and relies more on the GERT forecast. As a result, there is less deviation from the true trajectory, which is confirmed by the lower RMSE value (0.72) compared to the RMSE of the Kalman filter (0.98).  
In addition, the orange lines indicate the activation of the adaptive mode after the analysis of the interference features, which demonstrates the real-time response of the system.  
The results of calculating the efficiency metrics of the intelligent method of tracking targets in the radar at the initial stage of operation in conditions of intentional interference, which are performed as a result of simulation modeling, are given in Table 4.  
As we can see in Table 3, the proposed method demonstrates \(\approx\)25% lower tracking error and provides an operational response (delay of 1.25 cycles), unlike the Kalman filter, which does not adapt to changes in the environment at all.