## 2 Materials and Methods
### Structural Model of Interaction of the GERT Model, Kalman Filter and Intelligent Module
In Figure 1, arrow 1 between the input measurements and the GERT model demonstrates the transmission of raw measurement data used to construct the probabilistic state model. Arrow 2 shows the transmission of the initial estimate (coordinates, probabilities, timing) from the GERT model to initialize the Kalman filter. Arrow 3 illustrates the output of the final smoothed trajectory estimate from the Kalman filter to the output block. Arrow 4 shows the flow of control signals from the intelligent module to the Kalman filter for adaptive adjustment of filtering parameters. Arrow 5 represents the transmission of probabilistic indicators and transition scenarios from the GERT model to the intelligent module, which are considered during decision making. The GERT model is the basis for forming a probabilistic description of the process of detecting and tracking targets at the initial stage of radar operation. In previous studies [6], a network was constructed whose nodes corresponded to the key states of the system (target detection, formation of the initial trajectory, loss of support, etc. ); the arcs characterize the probability of transition between these states and the corresponding time distribution. As a result of calculating the parameters of the GERT network [6], the following parameters were obtained:  
* the expected time of interaction of the system with the target before transition to a stable support mode;
* the probabilities of successful detection and loss of support;
* variances of the time spent in each state, reflecting the level of uncertainty. Figure 1: Structural diagram of the interaction of the components of the intelligent method of tracking targets in a radar station at the initial stage of operation in conditions of intentional interference. These parameters allow the initial prediction zone to be formed, in which the object is most likely to be located after the detection stage. The uncertainty zone is the starting point for further processing in the Kalman filter. Thus, the results of the GERT model are used to initialize the state vector and the covariance matrix of the filter errors, which ensures the adaptation of the tracking algorithm to the real level of interference and detection characteristics. The Kalman filter is a classic tool for recursive estimation of the object's motion parameters in the case of noisy measurements. Within the framework of the proposed method, the filter is used after GERT initialization for local refinement of the target coordinates and for the estimation of the speed and direction of movement. The filter algorithm consists of two main stages: state prediction and its correction based on new measurements. In this case, the key parameters that determine the efficiency of the filtering are as follows:  
* the covariance matrix of the prediction error (\(P\));
* process noise covariance (\(Q\));
* measurement noise covariance (\(R\)). These parameters can vary depending on the intensity of interference, the frequency of signal loss, or measurement instability. In order to adapt the filter to external conditions, the intelligent module evaluates the residual statistics, the rate of change of state parameters, and the level of confidence in the input information, forming recommendations for updating \(Q\) and \(R\) in real time. At the stage of interaction with the GERT model, the filter uses not only the initial uncertainty zone, but also an estimate of the probability of loss of tracking, which may signal the need to reduce the weight of current measurements or switch to an alternative prediction mode. Thus, the Kalman filter in the proposed architecture plays the role of a target trajectory refiner, adapting to the context given by the GERT network and controlled by the intelligent module. The proposed structure is an extension of the functional algorithm presented in [6].