A frequency estimation algorithm based on cross information fusion

Abstract

To improve frequency estimation accuracy, a frequency estimation algorithm based on cross information fusion was proposed. The algorithm was suitable for signals of short duration and low signal-to-noise ratio (SNR), which are common in engineering. Firstly, several different signal groups were obtained by grouping multisegment signals according to the guidelines of combination. Secondly, rotation factors were obtained according to complementary information of multisegment signals in each signal group. Thirdly, the average cross information fusion spectrum was achieved by the arithmetic mean value of all the cross information fusion spectra, which were calculated according to rotation factors. Finally, the average cross information fusion spectrum was peak-picked to get the frequency estimator. Algorithm analysis and experimental results showed that compared with the existing algorithms, the proposed algorithm was superior in precision, robustness, and noise immunity.

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

“…On this basis, Tu et al. proposed a multi‐segment signal spectrum fusion algorithm based on phase compensation [8–12]. By setting the phase difference compensation factor, the signal spectrum with narrow main lobe and more concentrated energy can be obtained.…”

“…The spectral averaging [7], as a common spectrum fusion method, can effectively improve the stability of frequency measurement by taking the mean of each spectrum for frequency estimation, but the main lobe of the obtained spectrum is wide, thus the estimation accuracy needs to be improved. On this basis, Tu et al proposed a multi-segment signal spectrum fusion algorithm based on phase compensation [8][9][10][11][12]. By setting the phase difference compensation factor, the signal spectrum with narrow main lobe and more concentrated energy can be obtained.…”

A method for fast estimation of slowly changing parameters of a quasi-harmonic signal

“…In all varieties of approaches to the problem of estimating the signal parameters (frequency and amplitude) there are differences primarily in estimation accuracy, computational complexity and processing delay [1,[44][45][46]. It is impossible to meet all the specified criteria simultaneously even if we pre-process the signal (for example, noise filtering) or use adaptive algorithms or use a priori information about the signal and other optimal solutions [47].…”