Optimized window functions for spectral analysis based on digital filters

The article addresses a relevant issue of improving the accuracy of spectral analysis in computerized systems by optimizing window functions used in the Discrete Fourier Transform (DFT). One of the main challenges in real-world signal spectral analysis is spectral leakage, which causes the appearance of unwanted side lobes in the signal spectrum. To mitigate this effect, traditional window functions (such as Hanning, Hamming, Blackman, Kaiser, etc.) are typically applied. However, these functions have certain limitations, and their use in many practical tasks does not always provide the required level of accuracy. A novel approach is proposed for constructing window functions through the synthesis of Finite Impulse Response (FIR) digital filters. This provides a more flexible tool for adapting to the specific features of the analyzed signals. The use of standard FIR filter design methods is demonstrated. A comparative analysis of the spectral characteristics of the synthesized windows and classical window functions has been performed. Modeling results show that the synthesized windows can achieve lower side lobe levels while maintaining the same main lobe width, which directly contributes to reduced spectral estimation errors. The findings are essential for enhancing the performance of computerized systems for power quality monitoring. The proposed approach also shows promising potential for further development through the integration of machine learning techniques and heuristic algorithms, enabling automatic tuning of window parameters depending on analysis conditions.

Abstract licence: CC BY-NC

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