## 2 Materials and Methods
### Model for Assessing the Reliability of Measurements Using LSTM Networks
For each simulated feature window, a reliability label is created (0--measurement is expected to be noisy or absent, 1--reliable signal reception is expected). The training set contains at least 10,000 examples, with class balancing using oversampling or weighting coefficients in the loss function. Table 1 shows the hyperparameters of the model. To improve the quality of learning, the features in the training sample were formed taking into account the true value of \(P_{meas}(t+1)\). In particular, in the case of a reliable signal, the innovation values were simulated with a low noise level (\(\sigma=0.5\)), the covariance remained stable, and the measurement losses were absent or single. In the case of noise or loss, the noise level increased significantly (\(\sigma=2.0\)), and the number of missed measurements and the average duration of losses increased significantly. The simulation allowed us to clearly separate the classes and ensure high-quality training of the neural network. The training was carried out in the TensorFlow/Keras environment. The model was trained on artificially generated sequences with specified signal reliability characteristics. The effectiveness was assessed by visually comparing the predicted values of \(P_{meas}(t+1)\) with the reference sequence. Figure 7 shows a fragment of the model forecast. This figure shows that in stable conditions, the forecast values are kept at a high level, while with increasing interference or signal loss, the model promptly reduces the reliability estimate to a minimum. This reaction indicates the ability of the LSTM model to recognize potentially critical changes in measurements based on input features. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \# & **Parameter** & **Value** \\ \hline
1 & Input window size & 10 cycles \\ \hline
2 & Number of LSTM units & 32 \\ \hline
3 & Number of features & 5 \\ \hline
4 & Dropout & 0.2 \\ \hline
5 & Output activation & Sigmoid \\ \hline
6 & Loss function & Binary crossentropy \\ \hline
7 & Optimizer & Adam \\ \hline
8 & Batch size & 64 \\ \hline
9 & Epochs & 30 \\ \hline \end{tabular}
\end{table} 
Table 1: Hyperparameters of the model. Figure 7: Comparison of the true and predicted values of \(P_{meas}(t+1)\) in a simulated scenario. It is also worth noting that in some simulation intervals, the predicted probability values drop to zero. This behavior corresponds to time periods where the input features indicate stable measurement conditions with no apparent signs of degradation. In such cases, the model correctly predicts a minimal risk of signal loss, resulting in zero output values. These are both expected and valid outcomes that demonstrate the model's ability to distinguish between high-risk and low-risk states. Thus, the results of training and testing the LSTM model confirm its ability to detect signs of a decrease in the reliability of measurements based on internal filtering features in a timely manner. The resulting forecast value \(P_{meas}(t+1)\) can serve as an indicator for dynamic control of the monitoring mode, reconfiguration of filtering parameters, and adaptation of the signal-processing logic. However, in order for the predicted estimate to effectively affect the operation of the entire monitoring system, it is necessary to implement a reaction mechanism, namely, the adaptation of filtering parameters, weighting of estimates, or even changing the monitoring mode. This is especially important in situations where the level of interference changes rapidly or has a complex structure. Thus, an intelligent module for predicting the reliability of measurements was developed and substantiated, combining classical filtering algorithms (Kalman filter) with elements of artificial intelligence (LSTM). Next, we will consider the method of adapting parameters in real time, taking into account the predicted operating conditions, interference level, and context of object dynamics.