## 1 Introduction
### State of the Art
However, the method does not provide for interactive adaptation or the correction of parameters in real time, which reduces its effectiveness in dynamic tracking systems. Among researchers, there has been a rapid increase in the use of artificial intelligence models, in particular, recurrent neural networks (RNN), LSTM, and their modifications, for the processing and classification of radar signals. These models have the ability to detect temporal patterns, respond to signal instability and form predictive estimates, which is especially relevant in tracking problems with losses or interference. In [9], the use of LSTM for sorting radar signals and individual source recognition is proposed. The model is trained on sequences containing information about the signal signature and allows for the accurate identification of objects in a complex environment. The advantage is high sensitivity to the signal structure, as well as the ability of LSTM to retain memory of previous states. At the same time, the model is focused mainly on classification, and it is not integrated into the tracking or filtering process of coordinates, which limits its direct application in trajectory tracking. In [10], the problem of spatiotemporal forecasting of radar echoes using convolutional LSTM (ConvLSTM), which combines the properties of the convolutional neural network (CNN) and recurrent neural network (RNN), is considered. The model uses multi-source data (satellite, sensor), achieving high forecast accuracy in heterogeneous environments. The study showed its ability to work with multidimensional time series and the exploitation of both spatial and temporal dependencies. However, the work is focused on meteorological applications and does not consider adaptation to measurement losses or dynamic responses to interference, which significantly limits its relevance for radar tracking tasks. In [11], an LSTM framework for classifying radar and communication signals based on their statistical and spectral characteristics is presented. The authors use the spectral decomposition of the signal as an input vector to the LSTM, achieving high recognition accuracy in noisy environments. A strong aspect of the framework is its ability to detect anomalies or interference signals without manually setting thresholds, which can be useful when assessing the reliability of measurements. However, the model does not work with coordinate information and does not implement trajectory estimation or correction, which is key for tracking tasks. Thus, works [9; 10; 11] confirm the effectiveness of LSTM architectures in detecting patterns, recognizing signal sources, and predicting behavior in time series. However, most of them are not integrated into the tracking systems of the object coordinates and do not take into account dynamic adaptation in real time, which was one of the motivating factors for the development of the adaptive method proposed in this work. In [12], a fuzzy gain-controlled filter is proposed, which is used for tracking in difficult conditions when the signal is distorted or intermittent. The advantage of the approach is the ability to dynamically change the filtering coefficients, depending on the degree of observation reliability, without the need for an exact mathematical description of the noise. The method demonstrates better stability compared to traditional filters, but it does not include a long-term prediction or learning mechanism, which reduces its flexibility in cases where the signal structure changes over time. In [13], adaptive fuzzy tracking control for nonlinear MIMO systems with state constraints and unknown control directions is presented. This approach combines classical control with fuzzy logic to achieve high control accuracy even under complex object dynamics. The main advantage is the ability to adapt to incomplete object models and limited observations, which makes the method relevant for conditions with an unstable signal.