## 4 Discussion  
The obtained results can be explained by the features of the construction of the proposed architecture of the tracking system, in which a coordinated interaction between the GERT model, the Kalman filter, and the intelligent adaptation module is implemented. In particular, Figure 1 shows the structure of the method, which allows for the use of probabilistic information about the state of the system for initialization (GERT), local recursive processing (Kalman filter), and dynamic control (intelligent module). Table 1 illustrates how the system operating mode changes based on the prediction of the reliability of measurements, which ensures its adaptability. The reduction in the mean square error of the tracking and the increase in the robustness to interference are confirmed by the results shown in Figures 3 and 7. The advantages of the proposed method lie in its ability to perform dynamic weighting between the estimates of the Kalman filter and the GERT model (Figure 2), which maintains accuracy even when measurements are lost. This method takes into account a complex context, including error trends, the probability of tracking loss (Figure 3), and the forecast of the LSTM model (Figure 6). This becomes possible due to the intelligent module that implements combined analysis using fuzzy logic and neural networks [29; 30; 31]. Real-time adaptation of the filtering parameters based on the reliability forecast (Formula (7)) solves the problem of tracking loss under active interference conditions. Dynamic mode control (Table 1) provides smooth switching between standard, robust and predictive support, which allows for the avoidance of critical failures even without measurements. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Method** & _RMSE_ (\(\mathbf{x}\)) & \
\begin{tabular}{c} **Probability of Losing** \\ **Support \(P_{\mathit{lost}}\)** \\ \end{tabular} &
\begin{tabular}{c} **Average Response** \\ **Latency \(D_{\mathit{response}}\)** \\ \end{tabular} \\ \hline Kalman Filter & 0.975 & 0.2 & No response \\ \hline Adaptive Fusion & 0.724 & 0.1 & 1.25 cycles \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of calculating the efficiency metrics of the intelligent method. Figure 11: Comparison of the reference trajectory \(x_{\mathit{true}}(t)\) with the original smoothed estimate \(\hat{x}(t)\). Additionally, we acknowledge that the current evaluation is limited to single-target tracking scenarios. While this setting allows for a focused analysis of adaptive filtering and robustness under jamming, it does not account for challenges specific to multi-target tracking (MTT) such as false associations, track coalescence, or target loss due to ambiguous measurements. These effects become especially critical in the presence of interference. Future work will address the extension of the system to MTT configurations, incorporating data association mechanisms, track-management logic, and advanced fusion algorithms to handle multiple interacting targets under jamming conditions. From a hardware perspective, we acknowledge that the use of an LSTM network with 32 units and a 10-cycle time window may introduce latency, especially in high-frequency radar systems (e.g., 100 Hz). However, in the proposed architecture, the LSTM module functions as an auxiliary component for forecasting measurement reliability and is not required to operate at every cycle. This reduces the real-time processing burden. Furthermore, future development will focus on optimizing model size and complexity, evaluating alternative architectures, such as GRU or temporal convolutional networks, and applying compression techniques such as quantization and pruning. Recent studies have demonstrated the feasibility of deploying optimized LSTM networks on embedded and FPGA-based platforms with acceptable latency and energy efficiency [32]. Among the limitations of the study, we can outline the dependence of efficiency on the correctness of training the LSTM model.