scholarly journals Multidimensional Single-Tone Frequency Estimation Based on QR Decomposition

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 68153-68159
Author(s):  
Hui Cao ◽  
Long-Ting Huang ◽  
Yuntao Wu ◽  
Qi Liu
Keyword(s):  
Frequency Estimation ◽  
Qr Decomposition ◽  
Tone Frequency ◽  
Single Tone

FFT-Based Moving Average Maximum Likelihood Single-tone Frequency Estimation

2011 ◽  
Vol 30 (4) ◽  
pp. 831-835
Author(s):  
Yu-chun Huang ◽  
Zai-lu Huang ◽  
Ben-xiong Huang ◽  
Shu-hua Xu
Keyword(s):  
Maximum Likelihood ◽  
Frequency Estimation ◽  
Moving Average ◽  
Tone Frequency ◽  
Single Tone ◽  
Average Maximum

scholarly journals Accurate Frequency Estimation Based On Three-Parameter Sine-Fitting With Three FFT Samples

2015 ◽  
Vol 22 (3) ◽  
pp. 403-416 ◽  
Author(s):  
Xin Liu ◽  
Yongfeng Ren ◽  
Chengqun Chu ◽  
Wei Fang
Keyword(s):  
Fourier Transform ◽  
Frequency Estimation ◽  
Signal To Noise Ratio ◽  
Golden Section ◽  
Least Square ◽  
Estimation Accuracy ◽  
Tone Frequency ◽  
Single Tone ◽  
Dc Component ◽  
Fitting Error

Abstract This paper presents a simple DFT-based golden section searching algorithm (DGSSA) for the single tone frequency estimation. Because of truncation and discreteness in signal samples, Fast Fourier Transform (FFT) and Discrete Fourier Transform (DFT) are inevitable to cause the spectrum leakage and fence effect which lead to a low estimation accuracy. This method can improve the estimation accuracy under conditions of a low signal-to-noise ratio (SNR) and a low resolution. This method firstly uses three FFT samples to determine the frequency searching scope, then – besides the frequency – the estimated values of amplitude, phase and dc component are obtained by minimizing the least square (LS) fitting error of three-parameter sine fitting. By setting reasonable stop conditions or the number of iterations, the accurate frequency estimation can be realized. The accuracy of this method, when applied to observed single-tone sinusoid samples corrupted by white Gaussian noise, is investigated by different methods with respect to the unbiased Cramer-Rao Low Bound (CRLB). The simulation results show that the root mean square error (RMSE) of the frequency estimation curve is consistent with the tendency of CRLB as SNR increases, even in the case of a small number of samples. The average RMSE of the frequency estimation is less than 1.5 times the CRLB with SNR = 20 dB and N = 512.

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