## 1 Introduction
### State of the Art
Nevertheless, the model is not directly oriented to radar or coordinate filtering; it requires adaptation for use in air target tracking tasks. Ref. [14] provides a thorough review of approaches to the use of artificial intelligence in radar resource management. The work analyzes a wide range of methods from reinforcement learning to neural network adapters that dynamically change the parameters of sensor deployment, trajectory update frequency, distribution of computing power, etc. The strengths of the work include a comprehensive vision of the future of radar systems as cognitive networks with a high level of autonomy. However, the study is more of a survey and does not contain implementations or integrated structures that combine classical state estimation algorithms with neural or fuzzy modules within a single accompanying filter. In [15], the relevance of the problem of increasing the accuracy of mathematical modeling results is determined. Methods of fuzzy GERT modeling are analyzed. The need for, and possibility of, increasing the accuracy of the results of mathematical formalization in conditions of fuzzy input and intermediate data are determined. However, the research has a direction related to software testing and, accordingly, requires additional research in the direction of the formalization of target-tracking processes in radar systems. Another critical aspect often overlooked in radar tracking studies is range ambiguity, which is especially relevant for pulse-Doppler systems operating under intentional jamming. When multiple echoes from different pulses overlap, it becomes difficult to distinguish true target returns from ambiguous ones. This problem is amplified in the presence of intentional interference and multi-path effects. Advanced suppression techniques based on underdetermined blind source separation were proposed in [16] to mitigate such ambiguities, particularly in SAR systems. Although our current model does not simulate range ambiguity directly, future development of the tracking framework may benefit from integrating similar techniques to improve robustness under ambiguous scenarios. Thus, the analysis of sources indicates the presence of local solutions that cover individual aspects of tracking in conditions of interference: filtering (Kalman, robust models); probabilistic modeling (GERT, IPDA, Markov); machine learning methods (LSTM); and fuzzy control. At the same time, the lack of a single consistent architecture that combines adaptability, forecasting, and accuracy in one method capable of operating in real time with the loss of measurements and changing interference is revealed. The general unsolved problem is the lack of a comprehensive approach to tracking air targets in complex conditions that dynamically adapts the filtering parameters, predicts the reliability of signals, and changes the processing mode depending on the level of interference. All this demonstrates the need to conduct research considering the formation of an integrated intelligent target-tracking method that combines the advantages of GERT models, Kalman filters, and LSTM networks in a single flexible signal-processing system in conditions of intentional interference.