An adaptive window function based on enhanced cuckoo search optimization for finite impulse response filter design

Abstrak

This study introduced a modern approach involving an adaptive window function with the enhanced cuckoo search optimization (ECSO) algorithm for optimizing the finite impulse response (FIR) filter design by dynamically adjusting window parameters. This proposed method enhanced spectral performance, and improved accuracy, resolution, and reliability in spectral analysis. A mathematical model was developed for the adaptive window function, and the original cuckoo search optimization (CSO) algorithm was enhanced through adaptive step-size adjustment. Results demonstrated better spectral characteristics with narrower main lobes, lower sidelobes, and enhanced stopband attenuation, indicating computational efficiency, versatility, and robustness. Comparative analysis showed that the adaptive window function outperformed Kaiser, Gaussian, Tukey, and Chebyshev windows, exhibiting superior frequency selectivity, uniform amplitude response within the passband, and improved signal fidelity with reduced interference from neighboring frequency bands. Additionally, it demonstrated lower leakage factors, indicating reduced spectral leakage and better confinement of signal energy within the desired frequency range. This advancement in FIR filter design holds promise for various signal processing tasks and real-time applications, marking a significant milestone in signal processing innovation.