## 1 Introduction
### State of the Art
In modern research, special attention is paid to the creation of algorithms capable of maintaining high tracking accuracy in conditions of uncertainty, interference, and variable target dynamics. Thus, in ref. [4], a strategy for distributing radar resources in distributed radar networks is proposed to counteract deceptive interference during the tracking of multiple targets. The proposed approach focused on effective planning in conditions of resource constraints and active interference. The main advantage of this work is the high level of detail of the interference models, taking into account the multi-position structure and limitations on data transmission channels. Nevertheless, the study focused on resource management, and not on the adaptation of tracking algorithms or the assessment of the quality of measurements in interference. In [5], a new version of the adaptive robust cubic Kalman filter (ARCKF) was proposed for the tracking of maneuvering targets under conditions of model uncertainty and anomalous noise. The authors implemented a mechanism for automatic adjustment of the filter parameters based on the assessment of data reliability, which reduced sensitivity to noisy or incorrect measurements. It is important that the method combines the accuracy of nonlinear filtering with the flexibility of the robust approach, adapting to changes in the external environment. The main advantage is that the filter responds not only to the dynamics of the target, but also to the quality of the measurement signal, which is critically important in real conditions of radar operation. However, the model has increased computational complexity, which may limit its use in systems with strict time constraints or limited resources. In tracking tasks under conditions of incomplete information or interference, probabilistic approaches that allow for modeling of the behavior of the object or the functioning of the radar at the macro level are of particular importance. Such approaches can provide flexibility in the initial assessment of the situation, calculation of the probabilities of loss or non-detection, and are effective in complex scenarios. In [6], a GERT model of the initial stage of interaction between the radar and the air target was proposed. The model takes into account the probabilities of detection, uncertainty at the moment of radar excitation, delay. and signal loss. The main advantage of this work is the stochastic description of the logic of transitions between events, which is especially relevant for modeling systems with partial observation and misses. The limitation is that the model is implemented as an independent mathematical construct and is not directly integrated into adaptive filters or the neural network structures required for dynamic tracking. In [7], the authors developed an improved probabilistic data association algorithm, integrated probabilistic data association (IPDA), for tracking tasks in a complex environment with a large number of obstacles and false targets. The advantage of this work is the high accuracy of processing multiple observations, where classical filters do not cope. The model significantly reduces the risk of incorrect measurement matching, which is especially important in multi-point or multi-sensor tracking. However, the disadvantage is the difficulty of scaling the algorithm when the number of targets increases, as well as the fact that the algorithm does not take into account the measurement reliability parameters as a source of filter adaptation. In [8], the effectiveness of integrating Markov chains with uncertainty analysis in event prediction based on limited satellite data was demonstrated. Although the subject of the study is hydrological processes, the tools used are universal and can be adapted for radar tasks. The main advantage is the combination of stochastic estimation with risk analysis in situations of incomplete observations.