### State-of-the-Art  
Given the evolving challenges and diverse methodologies in radar system modeling, this section reviews key approaches and highlights gaps addressed in this study. The literature review should begin with the monograph [1], which serves as a foundational scientific work emphasizing the importance of radar system operation and the complexity of this process in the presence of interference. This work is a comprehensive study of a specific topic, aimed at providing in-depth knowledge and analyses. It covers various aspects, including definitions, historical context, current trends, challenges, and future implications. At the same time, the author of the monograph highlights the need for further research and practical actions to improve the efficiency of radar system operations. A review of the literature reveals a wide variety of approaches to mathematical modeling and current challenges in formalizing radar system data processing. Thus, in the article [2], the importance of using radar systems in areas such as air traffic control, automotive safety, and environmental monitoring is noted. The relevance of improving the efficiency of radar systems operation in the presence of interference is emphasized. The article highlights the variety of approaches to solving this problem, including innovative ones, such as \"Bioinspired Design\". Several studies present simulation results based on various formalizing concepts. For example, in article [3], the problem of detecting subspace random signals against correlated non-Gaussian noise is considered, using different levels of knowledge about the statistical characteristics of both target signals and interference. The noise is modeled using a complex Gaussian distribution. Unfortunately, the authors do not provide a practical implementation algorithm for this development, which somewhat limits the applicability of the results presented in this article. In [4], the authors present a mathematical formalization approach using an adaptive extended Kalman filter with strong tracking capabilities. They introduce adaptive damping elements to enhance filtering accuracy and reduce the divergence problems associated with strong tracking methods. While this improves the estimate quality during tracking, the model is sensitive to noise, and its effectiveness deteriorates in the presence of anomalous emissions. Additionally, the algorithm requires regular updates to adaptive damping factors, which increases computational costs and complexity. In [5], the authors develop a Multiple-Model Gaussian Mixture Probability Hypothesis Density (MMGM-PHD) filter to improve the tracking of multiple objects in scenarios with predetermined trajectories, particularly in land, sea, and air transport control. Byincorporating hybrid models, the authors account for target maneuverability, which is difficult to address using homogeneous models. However, this method may be less effective in scenarios where targets behave unpredictably or deviate from predetermined paths. The authors of article [6] use a Markov model and a universal finite automaton, which allow for expanding the options for assessing target detection capabilities. Among the strengths of the work are its well-structured presentation and discussion of existing challenges, as well as an innovative approach to performance evaluation using different levels of abstraction. However, the study does not provide sufficient data to fully assess the feasibility of the proposed methodology in various operational scenarios. This could be an area for future research focused on further optimizing methods in the field of radar system operation. In [7], the authors explore the use of artificial intelligence in radar signal estimation, proposing a denoising autoencoder (DAE) and deep neural network (DNN) scheme.