### State-of-the-Art
Simulations conducted at various signal-to-noise ratios demonstrate the DAE's ability to overcome limitations in existing techniques and open up new possibilities for high-resolution direction-of-arrival (DoA) estimation in complex environments. However, training deep neural networks requires large amounts of data, which can be challenging in resource-limited or data-scarce settings. A conference report in [8] discusses the development of a method for feature extraction in unmanned aerial vehicle (UAV) motion modeling using a variational autoencoder, further exploring artificial intelligence applications in air target tracking under external influences. In [9], the authors present another effective approach to mathematical formalization, focusing on managing telecommunication network parameters under uncertainty caused by both natural and anthropogenic factors. The study suggests that the principles used in telecommunication networks can be adapted for radar systems, offering insights into assessing radar timing, target detection accuracy, and resistance to interference. Several articles, including [10], address the issue of adaptive algorithms for generating and tracking target trajectories at different stratification levels. In [10], the authors focus on tracking maneuvering air targets, proposing an adaptive tracking algorithm that switches between uniform and maneuvering motion models. Although this approach offers a solution to the challenges posed by fuzzy models and target dynamics, the lack of comparative studies with similar algorithms limits the ability to evaluate its reliability. In [11], the authors propose an improved algorithm for multidimensional target tracking in aircraft radar systems using Doppler technology. The algorithm, based on a Bayesian approach and random finite set (RFS) theory, effectively suppresses interference in complex conditions, avoiding traditional computational complexities. Reducing computational complexity while maintaining accuracy and reliability is a common goal in mathematical formalization research. In [12], fuzzy GERT modeling methods are analyzed to increase the accuracy of process formalization in software vulnerability studies under uncertain conditions. This suggests the potential for applying similar approaches in radar system modeling. Further evidence of this potential is found in [13; 14], where the authors explore GERT network modeling to enhance the efficiency of mathematical formalization under uncertainty. In [15], the development of a pulsed Doppler radar system model using MATLAB 9.5/Simulink is presented, offering a comprehensive example of simulation modeling. The study confirms the relevance of the chosen research direction. In [16], the theoretical basis of radar operation, including range and angle tracking mechanisms, is presented. The study also explores radar jamming technologies such as noise and deception jamming and develops an electronic attack simulator. While simulation modeling proves effective at certain research stages, simulating electronic attack scenarios can be complex and resource intensive. Finally, in [17; 18], the authors analyze various factors affecting radar performance, including uncertainty in radar positions and interference screening. Despite providing valuable insights, these studies often overlook the importance of measuring the interaction time between the radar and tracked object as an indicator of radar efficiency. In comparison to existing model-based schemes, such as the approach by [19], which focuses on interference suppression strategies using Markov Chain Monte Carlo (MCMC) in low- and medium-interference environments, our proposed model emphasizes scenarios with high dynamic jamming. The use of GERT networks provides macro-level probabilistic analysis, while the Kalman filter enhances micro-level parameter refinement. Similarly, the model developed by [20], which leverages deep learning for trajectory optimization in radar systems, differs from our approach by focusing on machine learning-based methods.