High-Precision Frequency Estimation Using Continuous FFT Spectrum Refinement Algorithm

This paper presents a high-precision frequency estimation method based on continuous FFT spectrum refinement. The algorithm enhances frequency estimation accuracy to two decimal places through iterative spectral zooming and peak interpolation techniques. Unlike MUSIC and ESPRIT methods that require complex matrix operations, this approach reduces computational load substantially while maintaining high accuracy. The implementation typically involves zero-padding, frequency bin interpolation, and parabolic fitting around spectral peaks. Key advantages include:

- Maintains high efficiency when processing large datasets through optimized FFT computations and selective refinement regions

- Accurately discriminates closely-spaced frequency components using resolution enhancement techniques

- Applicable to various signal processing domains including communication systems and acoustic analysis, with MATLAB/Python implementations using fft(), interp1(), and polyfit() functions

By adopting this high-precision frequency estimation approach, researchers can achieve more accurate results across multiple domains while efficiently handling large-scale data processing. The method's practical implementation involves threshold-based peak detection followed by local spectrum refinement using chirp-z transforms or phase-based corrections. This technique holds significant potential for real-world applications where computational efficiency and precision are critical. We hope this discussion provides valuable insights into advanced frequency estimation methodologies.