A high-precision frequency estimation algorithm based on multi-segment signal fusion

As the current multi-segment signal fusion algorithms cannot meet the requirement of high-precision frequency estimation at different signal-to-noise ratios (SNRs), an adaptive fusion algorithm is proposed in this paper. At high SNR, an autocorrelation weighted fusion algorithm is utilised, which adopts the real-time weighted fusion of multi-segment autocorrelation signals, resulting in denoised sinusoidal signals. Then the frequency is obtained by minimising the error function based on coarse estimation. At low SNR, an improved spectrum fusion algorithm is adopted, which uses the three-parameter sine-fitting method to determine the phase difference compensation factor, and the multi-segment signal spectrum is weighted to obtain a long signal spectrum with continuous phase. As a result, the frequency can be estimated by spectral peak search. Simulation results show that the proposed algorithm, compared with existing algorithms, not only improves the estimation accuracy by about 20%-50% but also can perform high-precision frequency estimation for multi-segment signals of different lengths, frequencies, and abnormal situations.