A fine frequency estimation algorithm based on DFT samples and fuzzy logic for a real sinusoid

The study on the frequency estimation of a real sinusoid has been addressed in this article. Discrete-Fourier-transform (DFT)-based frequency estimation always uses the maximum bin of DFT as a coarse estimation followed by a fine estimation algorithm, which computes a correction term to compensate for the initial frequency offset caused by the coarse estimation. To enhance the frequency estimation accuracy, a fine estimation algorithm based on DFT samples and fuzzy logic (FL) is proposed in this study. Firstly, a modified phase-based algorithm is presented to estimate the sign of the correction term. Then, a main-lobe coefficient and a side-lobe coefficient are constructed using the amplitude of three spectral lines to calculate the correction term. A FL controller is utilised to generate a weighted factor to adjust the weight of the main-lobe coefficient and the side-lobe coefficient in the formula of the correction term. Compared with other existing fine algorithms, the proposed algorithm improves the correct probability of sign estimation by 10%–20%, and improves the estimation accuracy by 5%–8% at lower carrier-to-noise ratios (CNRs). In addition, the performance of the proposed algorithm is less affected by the initial frequency offset than other algorithms.