Improving accuracy of the spectral-correlation direction finding and delay estimation using machine learning

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.327021

Keywords:

spectral-correlation analysis, radio signal monitoring, signal parameter prediction, direction finding accuracy

Abstract

The object of the study is the process of radio signal delay and direction estimation using digital spectral-correlation analysis enhanced by machine learning. This process is essential for high-accuracy 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 learning-based 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 signal-to-noise 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, prediction-based parameter estimation is expected to improve processing speed in future implementations. The results can be applied in real-time electromagnetic monitoring, radio surveillance, and defense applications, especially under limited computing resources or varying noise conditions

Author Biographies

Nurzhigit Smailov, Institute of Mechanics and Mechanical Engineering named after Academician U. A. Dzholdasbekov; Satbayev University

Doctor PhD

Department of Radio Engineering, Electronics and Space Technologies

Vitaliy Tsyporenko, Zhytomyr Polytechnic State University

PhD

Department of Biomedical Engineering and Telecommunications

Zhomart Ualiyev, Institute of Mechanics and Mechanical Engineering named after Academician U. A. Dzholdasbekov; Satbayev University

Doctor PhD

Department of Higher Mathematics and Modeling

Аіnur Issova, Institute of Mechanics and Mechanical Engineering named after Academician U. A. Dzholdasbekov

Candidate of Physical and Mathematical Sciences

Zhandos Dosbayev, Institute of Mechanics and Mechanical Engineering named after Academician U. A. Dzholdasbekov; Satbayev University

Doctor PhD

Department of Radio Engineering, Electronics and Space Technologies

Yerlan Tashtay, Satbayev University

PhD, Head of Department

Department of Electronics, Telecommunications, and Space Technologies

Maigul Zhekambayeva, Satbayev University

PhD

Department of Software Engineering

Temirlan Alimbekov, Satbayev University

Department of Computer Science and Sofware Engineering

Rashida Kadyrova, Almaty Academy of Internal Affairs of the Republic of Kazakhstan named after Makana Esbulatova

Department of Cyber Security and Information Technology

Akezhan Sabibolda, Institute of Mechanics and Mechanical Engineering named after Academician U. A. Dzholdasbekov; Almaty Academy of Internal Affairs of the Republic of Kazakhstan named after Makana Esbulatova

PhD

Department of Cyber Security and Information Technology

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Improving accuracy of the spectral-correlation direction finding and delay estimation using machine learning

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Published

2025-04-30

How to Cite

Smailov, N., Tsyporenko, V., Ualiyev, Z., Issova А., Dosbayev, Z., Tashtay, Y., Zhekambayeva, M., Alimbekov, T., Kadyrova, R., & Sabibolda, A. (2025). Improving accuracy of the spectral-correlation direction finding and delay estimation using machine learning. Eastern-European Journal of Enterprise Technologies, 2(5 (134), 15–24. https://doi.org/10.15587/1729-4061.2025.327021

Issue

Vol. 2 No. 5 (134) (2025): Applied physics

Section

Applied physics

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Copyright (c) 2025 Nurzhigit Smailov, Vitaliy Tsyporenko, Zhomart Ualiyev, Аіnur Issova, Zhandos Dosbayev, Yerlan Tashtay, Maigul Zhekambayeva, Temirlan Alimbekov, Rashida Kadyrova, Akezhan Sabibolda

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