@ARTICLE{Duda_Krzysztof_Fast_2021,
 author={Duda, Krzysztof and Zieliński, Tomasz P.},
 volume={vol. 28},
 number={No 4},
 journal={Metrology and Measurement Systems},
 pages={661-672},
 howpublished={online},
 year={2021},
 publisher={Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation},
 abstract={A new solution to the problem of frequency estimation of a single sinusoid embedded in the white Gaussian noise is presented. It exploits, approximately, only one signal cycle, and is based on the well-known 2nd order autoregressive difference equation into which a downsampling is introduced. The proposed method is a generalization of the linear prediction based Prony method for the case of a single undamped sinusoid. It is shown that, thanks to the proposed downsampling in the linear prediction signal model, the overall variance of the least squares solution of frequency estimation is decreased, when compared to the Prony method, and locally it is even close to the Cramér–Rao Lower Bound, which is a significant improvement. The frequency estimation variance of the proposed solution is comparable with, computationally more complex, the Matrix Pencil and the Steiglitz–McBride methods. It is shown that application of the proposed downsampling to the popular smart DFT frequency estimation method also significantly reduces the method variance and makes it even better than the least squares smart DFT. The noise immunity of the proposed solution is achieved simultaneously with the reduction of computational complexity at the cost of narrowing the range of measured frequencies, i.e. a sinusoidal signal must be sufficiently oversampled to apply the proposed downsampling in the autoregressive model. The case of 64 samples per period with downsampling up to 16, i.e. 1/4th of the cycle, is presented in detail, but other sampling scenarios, from 16 to 512 samples per period, are considered as well.},
 type={Article},
 title={Fast one-cycle frequency estimation of a single sinusoid in noise using downsampled linear prediction model},
 URL={http://www.czasopisma.pan.pl/Content/121799/PDF-MASTER/art04_final.pdf},
 doi={10.24425/mms.2021.137701},
 keywords={frequency estimation, linear prediction, Prony method, smart DFT},
}