## 1 Introduction
### State of the Art  
Due to the increasing complexity of the interference environment and the need for high-precision tracking of aerial targets at the initial detection stages, recent years have seen a significant intensification of research efforts aimed at improving the robustness and adaptability of radar systems. An analysis of recent studies in the literature enables us to identify five key research directions that form the scientific foundation for the development of the proposed intelligent tracking method. The first group of studies [1; 2; 3] emphasizes the relevance of the problem and explores general approaches to target tracking in complex scenarios. These works introduce conceptual frameworks and system-level solutions but often do not consider measurement loss or online adaptability. Another direction focuses on methods to improve tracking accuracy under interference [4; 5]. These include robust filters and enhanced signal-processing algorithms. However, they are typically based on static models and may not handle dynamic changes or reliability fluctuations in the measurements. A separate research stream develops probabilistic models for describing detection and tracking processes under uncertainty [6; 7; 8]. These models contribute to formalizing noise and ambiguity but are rarely integrated with learning-based forecast systems or real-time decision-making components. The application of intelligent methods, such as neural networks in radar data processing [9; 10; 11], has shown promising results in pattern recognition and prediction. However,most of these techniques are used as standalone tools and not in combination with classical tracking systems. Finally, recent work explores the integration of classical methods and AI [12; 13; 14; 15], aiming to combine the strengths of both approaches. Although such systems improve flexibility, they often lack mechanisms for dynamic mode switching based on measurement reliability and prediction. In the following paragraphs, we provide a more detailed discussion of representative works from each of these five research directions. For each study, we highlight the core methodology, key contributions, and existing limitations that motivated the development of our proposed approach. A significant number of modern publications are devoted to the analysis of threats associated with the growing complexity of the interference environment, which affects the accuracy and stability of air object tracking. In [1] it is emphasized that the presence of active interference significantly reduces the effectiveness of traditional filters, especially at the initial stage of tracking. However, the mechanisms of prediction and confidence in measurements are not considered in this work. In addition, the issues of adapting filtration algorithms to sudden changes in interference intensity remain unresolved. This fact is also confirmed in work [2]. The article proves that even in the absence of direct signal loss, nonlinearity and changes in the dynamics of the object can lead to the accumulation of errors if the filter operates without adaptation. The authors proposed the automatic tuning of Kalman filter parameters, illustrating the general trend of the transition from static to adaptive systems. However, the model does not contain a predictive assessment of the reliability of measurements or control of the tracking mode. One review [3] summarized modern technological trends related to the improvement of radar architectures, in particular through the integration of artificial intelligence, cognitive mechanisms, and multimodal data processing. This review confirmed that the combination of classical filtering methods with intelligent components is one of the most promising directions in the development of new generation tracking systems. However, the article does not contain proposals for the implementation of models.