## 2 Materials and Methods
### Structural Model of Interaction of the GERT Model, Kalman Filter and Intelligent Module
If the previous algorithm described the standard sequence of primary and secondary signal processing in radar, then, within the framework of this method, this logic is supplemented by an intelligent adaptation layer that operates above the level of classical data processors. Instead of rigidly set parameters, the system dynamically changes its behavior in accordance with environmental conditions, ensuring tracking stability even at high levels of intentional interference. In modern conditions of the operation of radar systems for tracking air targets, the need to process signals in conditions of high levels of dynamic and complexly structured interference is increasingly emerging. These interferences can vary both in intensity and type (active, passive, pulsed, interfering, etc.) [17]. In such an environment, the use of static or fixed filtering parameters in traditional algorithms (in particular, the Kalman filter) leads to a significant decrease in tracking accuracy and stability. The Kalman filter, despite its effectiveness in normal noise conditions with known statistics, is sensitive to the wrong choice of process noise (\(Q\)) and measurement noise (\(R\)) covariances. In situations where these parameters remain constant and signal conditions change (e.g., sudden increase in the level of interference or loss of measurements), the filter generates either too inertial or unstable trajectory estimates. This can lead to loss of tracking. Moreover, the GERT model, which provides a probabilistic-temporal basis for the initialization, does not provide self-correction or reaction to changes in the environment in real time. This creates a gap between theoretical estimates at the start and the real behavior of the object under the influence of a changing interference environment. In this regard, there is a need to introduce an intelligent adaptive module that will:  
* assess the current state of the tracking system based on the analysis of filtering errors, measurement stability, and the statistical characteristics of residuals;
* predict potential situations of loss of tracking based on trends in changes in the accuracy of the estimate;
* adapt the filtering parameters (for example, adjust \(Q\) and \(R\), change the convergence coefficient between the estimates of the GERT model and the Kalman filter);
* provide flexible control of the logic of the tracking algorithm in accordance with external conditions. Such a module can be implemented on the basis of artificial intelligence elements, in particular, machine learning, fuzzy logic (fuzzy rule-based systems), and Bayesian adaptive structures. In real time, the intelligent module must perform cyclic analysis of the quality of tracking, compare current metrics with historical ones, identify deterioration trends, and predict risks of loss. The most important thing is to form recommendations or directly control the filtration parameters before the system loses efficiency. Due to this, the tracking system acquires self-tuning properties, which allows for high quality trajectory assessment to be maintained even in the complex and unstable conditions typical for the combat use of radar or in situations of active counteraction from electronic warfare. Thus, the obtained phase transition structure allows for the behavior of the system to be formalized at the initial stage of tracking and also enables the system to determine the context in which the filtering characteristics can change. This structure underlies the adaptive logic implemented in the next stage--the dynamic estimate fusion mechanism--where loss or recovery scenarios influence the choice of the algorithm's operating mode. All experiments described in this paper were conducted using synthetic data generated via simulation. Specifically, the true target trajectory was generated using a random walk process; measurements were simulated by adding Gaussian noise with a standard deviation \(\sigma=2.0\).