Improving accuracy of the spectral-correlation direction finding and delay estimation using machine learning - Smailov, Nurzhigit; Tsyporenko, Vitaliy; Ualiyev, Zhomart; Issova Аіnur; Dosbayev, Zhandos; Tashtay, Yerlan; Zhekambayeva, Maigul; Alimbekov, Temirlan; Kadyrova, Rashida; Sabibolda, Akezhan (Eastern-European Journal of Enterprise Technologies, 2025)
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