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Fast and accurate frequency estimation is significant for instrumentation and measurement. A sinusoid frequency estimator using discrete Fourier transform (DFT) is presented. DFT is implemented on the sinusoid and the maximum DFT bin is sought out to obtain the coarse estimate. Different from all the existing methods, two asymmetric discrete-time Fourier transform (DTFT) samples situated at arbitrary positions on the same side of the maximum DFT bin are employed to get the fine estimate. The theoretical mean square error is analyzed. To evaluate the estimation performance, the presented estimator is compared with the Cramer–Rao lower bound (CRLB) and state-of-art estimators through computer simulations. Simulation results demonstrate that the presented algorithm is closer to the CRLB compared with the competing methods when the signal-to-noise ratio (SNR) varies in a large range and is unbiased at high SNR.

A sinusoidal frequency estimator based on interpolated Discrete Fourier Transform (DFT) algorithm by using Maximum Sidelobe Decay (MSD) windows is proposed in this paper. Firstly, the received sinusoid is weighted by an appropriate MSD window. Then DFT is carried out on the weighted sinusoid and the coarse estimation is acquired by finding the position of the maximum DFT sample. Different from all the existing algorithms, the presented estimator adopts the maximum DFT sample and two Discrete Time Fourier Transform (DTFT) spectral lines which are on the same side of the maximum DFT sample in the fine estimation step. MSE formulas of the presented estimator in additive white noise are derived. Simulation results indicate that the presented estimator outperforms the competing estimators.

An accurate frequency estimation method of multi-component sinusoidal signal in additive white Gaussian noise (AWGN) is proposed. The algorithm is implemented in the frequency domain and based on discrete Fourier transform (DFT). The maximum DFT spectral line and two Discrete-Time Fourier Transform (DTFT) samples located on the same side of the maximum DFT spectral line are used to estimate the frequency of a sinusoidal component. And this algorithm is utilized in both the coarse estimation and the fine estimation. Simulation results show that compared with the competing estimators, the presented method is closer to the Cramer-Rao lower bound (CRLB). And it is almost independent of the frequency displacement. The numerical complexity of the presented method is similar with the competing DFT-based algorithms.