## 2 Materials and Methods
### Model for Assessing the Reliability of Measurements Using LSTM Networks
In the classical Kalman filter, the forecast accuracy and the response speed of the system are determined by the characteristics of the process noise \(Q\) and the measurement \(R\). In stable conditions, the value of \(R\) can be low, which allows the model to actively trust the measurements. However, as interference or signal loss increases, \(R\) must be increased to reduce the weight of observations  
At each monitoring cycle, the system checks the forecast \(P_{mass}\) and the latest loss characteristics, after which it selects the mode and updates the corresponding filter parameters (\(Q\), \(R\)) and the estimation fusion strategy. Figure 9 presents an illustration of a typical scenario of adaptation of the tracking system in conditions of deterioration of the reliability of measurements, presented as a graphic accompaniment to the case study. In this figure, the cycle 0-9 illustrates that the system operates in the standard mode: the predicted reliability of measurements is \(P_{mass}(t+1)\approx 1\), the matrix \(R\) is basic, and the Kalman coefficient is high (\(\approx\)0.8), i.e., the measurements have the maximum impact. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Operating Mode** & **Condition 1: Predicted Reliability \(P_{meas}(t+1)\)** & **Condition 2: Number of Missed Measurements in the Last N Cycles** & **System Actions** \\ \hline Standard & \(P_{meas}(t+1)>0.7\) & \(\leq\)1 & Normal mode. Full confidence measurements, minimum R values \\ \hline Robust & \(0.3<P_{meas}(t+1)\leq 0.7\) & =2 & RRR increase, limited measurement influence, partial model confidence offset \\ \hline Predictive & \(P_{meas}(t+1)\leq 0.3\) & \(\geq\)3 & Measurements ignored, estimation based on forecast (GERT or Kalman filter without update) \\ \hline \end{tabular}
\end{table} 
Table 2: Criteria for switching between the radar operating modes based on the reliability of measurements and signal losses. Figure 8: Block diagram of the algorithm for switching between the modes of operation of the radar station. During the cycle 10-19, the increase in interference is simulated. At the same time, the probability \(P_{meas}\) drops to \(\approx\)0.2. The system switches to the robust or predictive mode. The value of \(R\) increases sharply, reducing the weight of measurements. The Kalman coefficient decreases, reducing the impact of observations. During the cycle 20-29, the measurements stabilize. \(P_{meas}\) increases. The system gradually returns to normal mode, reducing \(R\) and restoring confidence in the measurements. Thus, the mechanisms of adaptation of target-tracking parameters in the radar based on the predicted reliability of measurements are considered. The system dynamically changes the values of the filtration parameters and the operating mode, responding to changes in conditions in real time. However, for a comprehensive assessment of the tracking efficiency, only local adjustment of the parameters is not enough. It is necessary to integrate the results from all subsystems (Kalman filter, GERT, artificial intelligence module) in order to form a single smoothed trajectory estimate, as well as to calculate tracking accuracy metrics. We will describe the algorithm for forming the initial estimate, as well as the method for calculating the error, the probability of loss of tracking and the response delay of the system.