IMPROVING ACCURACY OF THE SPECTRAL-CORRELATION DIRECTION FINDING AND DELAY ESTIMATION USING MACHINE LEARNING
Smailov N. Tsyporenko V. Ualiyev Z. Issova A. Dosbayev Z. Tashtay Y. Zhekambayeva M. Alimbekov T. Kadyrova R. Sabibolda A.
2025Technology Center
Eastern-European Journal of Enterprise Technologies
2025#2Issue 5(134)15 - 24 pp.
The object of the study is the process of radio signal delay and direction estimation using digital spectralcorrelation analysis enhanced by machine learning. This process is essential for highaccuracy direction finding in electromagnetic monitoring systems. The problem addressed is the low adaptability and insufficient accuracy of traditional direction finding methods under variable signal conditions, especially due to manual parameter selection and the computational complexity of correlation processing. The essence of the obtained results is a machine learningbased method for predicting radio signal parameters (delay and angle), which reduced the standard deviation of direction finding estimates to 0.08–0.026° and delay estimation error to 1.5–14.8 μs across a signaltonoise ratio range of 9 to 37 dB. These results are supported by averaging over 1000 realizations using Monte Carlo simulation, confirming their stability under noise. Due to its distinctive features, the proposed solution addressed the problem by enabling automated selection of processing parameters through a trained neural network that adapts to nonlinear signal characteristics, minimizing the need for manual adjustment or exhaustive search. These results are explained by the model’s ability to identify hidden dependencies between signal parameters and processing outcomes, enabling adaptive behavior and reduced deviations. Although no computational complexity assessment is provided, predictionbased parameter estimation is expected to improve processing speed in future implementations. The results can be applied in realtime electromagnetic monitoring, radio surveillance, and defense applications, especially under limited computing resources or varying noise conditions Copyright
direction finding accuracy , radio signal monitoring , signal parameter prediction , spectralcorrelation analysis
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Department of Radio Engineering, Electronics and Space Technologies
Department of Biomedical Engineering and Telecommunications, Zhytomyr Polytechnic State University, Chudnivska str., 103, Zhytomyr, 10005, Ukraine
Department of Higher Mathematics and Modeling
Candidate of Physical and Mathematical Sciences
Department of Electronics, Telecommunications, and Space Technologies
Department of Software Engineering
Department of Computer Science and Sofware Engineering
Institute of Mechanics and Mechanical Engineering named after Academician U. A. Dzholdasbekov, Kurmangazy str., 29, Almaty, 050010, Kazakhstan
Satbayev University, Satbayev str., 22, Almaty, 050013, Kazakhstan
Department of Cyber Security and Information Technology, Almaty Academy of Internal Affairs of the Republic of Kazakhstan named after Makana Esbulatova, Utepov str., 29, Almaty, 050060, Kazakhstan
Department of Radio Engineering
Department of Biomedical Engineering and Telecommunications
Department of Higher Mathematics and Modeling
Candidate of Physical and Mathematical Sciences
Department of Electronics
Department of Software Engineering
Department of Computer Science and Sofware Engineering
Institute of Mechanics and Mechanical Engineering named after Academician U. A. Dzholdasbekov
Satbayev University
Department of Cyber Security and Information Technology
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