## 1 Introduction
### Objectives and Contribution  
The purpose of the study is to develop an intelligent method for tracking air targets, capable of adapting to changes in the interference environment, and maintaining the accuracy of coordinate estimation in difficult conditions of radar operation. This will make it possible to ensure the predicted stability of tracking even in cases of partial loss of measurements and reduce the probability of target loss due to dynamic control of signal-processing modes.  
To achieve the purpose, the following tasks were set:  
* determine the structure of the interaction of the GERT model, the Kalman filter and the intelligent module;
* develop an algorithm for dynamic weighting between the estimates of the Kalman filter and the GERT model;
* develop a model for assessing the reliability of measurements using the LSTM network;
* develop a method for the initial assessment of the trajectory and accuracy of target tracking.  
The object of the study is the process of tracking an air target at the initial stage of the radar system's operation in conditions of intentional interference, characterized by incomplete data, a high level of interference, and the risk of loss of measurements.  
The main hypothesis of the study is the assumption that the combination of the GERT model, the Kalman filter and the intelligent module for predicting the reliability of measurements allows for the stable and accurate tracking of air objects in real time even under variable or adverse radar operating conditions.  
The following assumptions are made within the framework of the study:  
* the target motion model is linear, with known dynamics parameters (the linear model was chosen to simplify the analytical representation but the architecture of the method allows for adaptation to nonlinear models);
* the characteristics of noise in the process and in the measurements can be adaptively estimated using residual indicators;
* radar signals arrive at a fixed frequency, which allows for the implementation of recursive processing algorithms in real time.  
The adopted simplifications include:  
* the target motion is described in two-dimensional space (azimuth-range), which corresponds to the primary tracking mode in surveillance radars. The model focuses on the initial phase, where the elevation coordinate is either unavailable or processed separately in another channel;the influence of interference is reduced to losses or distortions of measurements without modeling the physical nature of the interference;
* delays in signal processing and data transmission are not taken into account as critical in the analyzed architecture.  
The selected simplifications do not reduce the overall validity of the results, since they focus on the most critical moment for the system, namely, the initial phase of target detection and capture, where tracking losses most often occur.  
The work uses analytical, numerical, and simulation methods. The theoretical justification is based on the application of the stochastic GERT model to describe the phases of target detection, recursive filtering of coordinates by the Kalman filter, and the prediction of measurement quality using the LSTM network. The integration of these components is carried out through the dynamic weighting of estimates and adaptation of filtration parameters. To test the hypothesis, a series of simulation experiments with variable interference conditions were conducted.