Comparative Analysis of Various Single-tone Frequency Estimation Techniques in High-order Instantaneous Moments Based Phase Estimation Method

2010 ◽  
Author(s):  
G. Rajshekhar ◽  
Sai Siva Gorthi ◽  
Pramod Rastogi ◽  
Pramod K. Rastogi ◽  
Erwin Hack
Keyword(s):  
Comparative Analysis ◽  
Frequency Estimation ◽  
Estimation Method ◽  
High Order ◽  
Phase Estimation ◽  
Tone Frequency ◽  
Single Tone ◽  
Estimation Techniques

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 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

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.

scholarly journals Experimental and Analytical Studies on Improved Feedforward ML Estimation Based on LS-SVR

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Xueqian Liu ◽  
Hongyi Yu
Keyword(s):  
Frequency Estimation ◽  
Additive White Gaussian Noise ◽  
Estimation Method ◽  
Threshold Value ◽  
Small Sample ◽  
Support Vector ◽  
Linear Filter ◽  
Ml Estimation ◽  
Single Tone ◽  
Estimation Performance

Maximum likelihood (ML) algorithm is the most common and effective parameter estimation method. However, when dealing with small sample and low signal-to-noise ratio (SNR), threshold effects are resulted and estimation performance degrades greatly. It is proved that support vector machine (SVM) is suitable for small sample. Consequently, we employ the linear relationship between least squares support vector regression (LS-SVR)’s inputs and outputs and regard LS-SVR process as a time-varying linear filter to increase input SNR of received signals and decrease the threshold value of mean square error (MSE) curve. Furthermore, it is verified that by taking single-tone sinusoidal frequency estimation, for example, and integrating data analysis and experimental validation, if LS-SVR’s parameters are set appropriately, not only can the LS-SVR process ensure the single-tone sinusoid and additive white Gaussian noise (AWGN) channel characteristics of original signals well, but it can also improves the frequency estimation performance. During experimental simulations, LS-SVR process is applied to two common and representative single-tone sinusoidal ML frequency estimation algorithms, the DFT-based frequency-domain periodogram (FDP) and phase-based Kay ones. And the threshold values of their MSE curves are decreased by 0.3 dB and 1.2 dB, respectively, which obviously exhibit the advantage of the proposed algorithm.

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