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Journal articles on the topic 'Frequency estimator'

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Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Zhou, Shen, and Liu Rongfang. "Efficient and Accurate Frequency Estimator under Low SNR by Phase Unwrapping." Mathematical Problems in Engineering 2019 (April 14, 2019): 1–6. http://dx.doi.org/10.1155/2019/7396074.

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In the case of low signal-to-noise ratio, for the frequency estimation of single-frequency sinusoidal signals with additive white Gaussian noise, the phase unwrapping estimator usually performs poorly. In this paper, an efficient and accurate method is proposed to address this problem. Different from other methods, based on fast Fourier transform, the sampled signals are estimated with the variances approaching the Cramer-Rao bound, followed with the maximum likelihood estimation of the frequency. Experimental results reveal that our estimator has a better performance than other phase unwrapping estimators. Compared with the state-of-the-art method, our estimator has the same accuracy and lower computational complexity. Besides, our estimator does not have the estimation bias.

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2

Zhu, Dong Xu, Jiu Ying Zhi, and Gang Fu. "A New Frequency Estimator in Multi-Frequency Estimation." Applied Mechanics and Materials 602-605 (August 2014): 3727–30. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3727.

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By analyzing the second-order and fourth-order IIR filter that affect the variance adaptive prove second-order IIR model has a single peak characteristics, to achieve stable convergence; while the higher-order IIR model polymorphic, so the need for the estimated parameters are initialized conclusions. Proposed use of FFT frequency estimation assisted IIR adaptive filter to achieve more frequent rate estimation, parameter estimation for the multi-point frequencies adaptive selection process provides a theoretical basis.

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3

Kuc, Roman, and Hilda Li. "Reduced-Order Autoregressive Modeling for Center-Frequency Estimation." Ultrasonic Imaging 7, no. 3 (July 1985): 244–51. http://dx.doi.org/10.1177/016173468500700304.

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The center frequency of a narrowband, discrete-time random process, such as a reflected ultrasound signal, is estimated from the parameter values of a reduced, second-order autoregressive (AR) model. This approach is proposed as a fast estimator that performs better than the zero-crossing count estimate for determining the center-frequency location. The parameter values are obtained through a linear prediction analysis on the correlated random process, which in this case is identical to the maximum entropy method for spectral estimation. The frequency of the maximum of the second-order model spectrum is determined from these parameters and is used as the center-frequency estimate. This estimate can be computed very efficiently, requiring only the estimates of the first three terms of the process autocorrelation function. The bias and variance properties of this estimator are determined for a random process having a Gaussian-shaped spectrum and compared to those of the ideal FM frequency discriminator, zero-crossing count estimator and a correlation estimator. It is found that the variance values for the reduced-order AR model center-frequency estimator lie between those for the ideal FM frequency discriminator and the zero-crossing count estimator.

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4

Zhu, Dong Xu, Jiu Ying Zhi, and Gang Fu. "Study on IIR Adaptive Frequency Estimator in the Multi-Frequency Estimation." Applied Mechanics and Materials 602-605 (August 2014): 3731–34. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3731.

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Propose a use of IIR adaptive frequency estimator to achieve multi-frequency estimation algorithm. Analysis IIR filter characteristics, indicating that the second-order IIR model has a single peak characteristics, to achieve stable convergence; while the higher-order IIR model polymorphic, so the need for the estimated parameters are initialized. More traditional phase locked loop algorithm, which has no mixing components, to achieve the advantages of full-band frequency estimation, frequency estimation accuracy under the same environment and phase locked loop fairly.

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5

Kou, Ming Xin, Jian Hua Lin, and Gang Fu. "Multi-Frequency Estimates Based on IIR Adaptive Frequency Estimator." Advanced Materials Research 989-994 (July 2014): 3985–88. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3985.

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Presents a IIR adaptive frequency estimator to achieve multi-frequency estimation algorithm. More traditional phase locked loop algorithm, which has no mixing components, to achieve the advantages of full-band frequency estimation, frequency estimation accuracy under the same environment and phase locked loop fairly.

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6

Zhang, Gangbing, Lu Jin, and Defeng (David) Huang. "A dichotomous search-based frequency estimator with generic analytical expression." Modern Physics Letters B 32, no. 34n36 (December 30, 2018): 1840095. http://dx.doi.org/10.1142/s021798491840095x.

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Fine resolution frequency estimation of a single-tone complex sinusoidal signal in the additive white Gaussian noise is of importance in many fields. In this paper, a generic analytical expression is proposed to refine the residual of a dichotomous search, leading to an estimator with much less iterations than the conventional dichotomous search estimator. Compared with other existing estimators, the proposed estimator has a better trade-off between performance and computational complexity. Simulation results demonstrate that the root-mean-square error (RMSE) of the proposed estimator is closer to the Cramer–Rao lower bound (CRLB) than other estimators over the whole frequency interval when the signal-to-noise ratio (SNR) is above a threshold.

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7

Herment, A., and J. F. Giovannelli. "An Adaptive Approach to Computing the Spectrum and Mean Frequency of Doppler Signals." Ultrasonic Imaging 17, no. 1 (January 1995): 1–26. http://dx.doi.org/10.1177/016173469501700101.

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Modern ultrasound Doppler systems are facing the problem of processing increasingly shorter data sets. Spectral analysis of the strongly nonstationary Doppler signal needs to shorten the analysis window while maintaining a low variance and high resolution spectrum. Color flow imaging requires estimation of the Doppler mean frequency from even shorter Doppler data sets to obtain both a high frame rate and high spatial resolution. We reconsider these two estimation problems in light of adaptive methods. A regularized parametric method for spectral analysis as well as an adapted mean frequency estimator are developed. The choice of the adaptive criterion is then addressed and adaptive spectral and mean frequency estimators are developed to minimize the mean square error on estimation in the presence of noise. Two suboptimal spectral and mean-frequency estimators are then derived for real-time applications. Finally, their performance is compared to that of both the FFT based periodogram and the AR parametric spectral analysis for the spectral estimator, and, to both the correlation angle and the Kristoffersen's [8] estimators for the mean frequency estimator using Doppler data recorded in vitro.

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8

Millar, Russell B. "A better estimator of mortality rate from age-frequency data." Canadian Journal of Fisheries and Aquatic Sciences 72, no. 3 (March 2015): 364–75. http://dx.doi.org/10.1139/cjfas-2014-0193.

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The Chapman–Robson and weighted-regression estimators are currently the two preferred methods for estimation of instantaneous mortality, z, from a cross-sectional sample of age-frequency data. They are derived under the assumption of steady-state population dynamics. Here, a new estimator is developed from a population model that explicitly includes annual variability in recruitment. The new estimator is trivial to implement using existing generalized linear mixed model software. It is vastly superior to both the Chapman–Robson and weighted-regression estimators under a wide range of simulation scenarios in which sources of variability include partial recruitment to the fishery, autocorrelated annual recruitment, variability in annual survival, ageing error, and sampling randomness. All estimators produced confidence intervals that had lower actual coverage than their nominal 95% coverage. Nonetheless, the new estimator had the highest actual coverage, and under some scenarios this was achieved with a narrowest confidence interval.

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9

Saber, Mohamed, and El-sayed M.El-Kenawy. "Design and implementation of accurate frequency estimator depend on deep learning." International Journal of Engineering & Technology 9, no. 2 (April 3, 2020): 367. http://dx.doi.org/10.14419/ijet.v9i2.30473.

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An Accurate, efficient, and stable system to estimate the unknown input frequency of a sinusoidal signal is presented. The proposed design solves the main drawback of the existing phase-based estimator which called a derivative estimator depend on deep learning. These limitations are the inability to estimate low frequencies and the large estimation errors for the frequencies near the Nyquist rate. A Brief mathematical analysis in discrete-time of the proposed system is presented. Proposed estimator performance when the input is a single sinusoid, multiple sinusoids in the presence of additive white Gaussian noise (AWGN) are provided. The accuracy of the proposed esti-mator is the result of dividing the dynamic range of estimation to three regions (low frequencies, middle frequencies, high frequencies) and specify a different formula to calculate the estimated frequency in each region. The boundaries of each region are determined by using a Grey wolf optimizer (GWO) which training bidirectional recurrent neural networks (BRNN) to select the best weights for the estimated frequency. The simulation results ensure the accuracy and validity of the proposed estimator compared to the traditional one. The hardware implementation of enhanced estimator using field-programmable gate array (FPGA), consumed 265 mW, and worked at 375 MHz.

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10

Quinn, B. G. "On Kay's Frequency Estimator." Journal of Time Series Analysis 21, no. 6 (November 2000): 707–12. http://dx.doi.org/10.1111/1467-9892.00205.

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11

Ferrari, A., J. Menez, and G. Alengrin. "Single frequency efficient estimator." Electronics Letters 27, no. 21 (1991): 1939. http://dx.doi.org/10.1049/el:19911203.

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12

Cui, Wen, Bo Li, and Jiaxu Zhang. "Improved Frequency Offset Estimation Based on the DRM System." Wireless Communications and Mobile Computing 2021 (May 4, 2021): 1–9. http://dx.doi.org/10.1155/2021/8851297.

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In this paper, an improved frequency offset estimation algorithm is proposed. It can effectively utilize pilot symbols in orthogonal frequency division multiplexing (OFDM) signals based on the Digital Radio Mondiale (DRM) system. In order to reduce the computational difficulty, the difference values of adjacent pilot symbols in the DRM system are sequentially combined into a pilot difference set. The pilot difference set is analyzed by inductive statistics so that the frequency offset estimator has higher calculation efficiency and accuracy. Through the induction statistics of multiple groups of symbols, the contingency of results can be effectively avoided. By comparing the performance of the proposed frequency offset estimator with that of the traditional estimators, the proposed algorithm has higher frequency offset estimation accuracy.

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13

Brands, Peter J., and Arnold P. G. Hoeks. "A Comparison Method for Mean Frequency Estimators for Doppler Ultrasound." Ultrasonic Imaging 14, no. 4 (October 1992): 367–86. http://dx.doi.org/10.1177/016173469201400404.

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Mean frequency estimators as used in pulsed Doppler ultrasound equipment should provide an accurate (quality) and consistent (robustness) estimate over a wide range of signal conditions. In a simplified signal model, the main parameters to consider are the noise level, mean frequency, bandwidth and power of both the Doppler signal and the stationary component over a given time window. It may be expected that one estimator for a given parameter combination exhibits a good performance while another estimator for the same parameter combination behaves poorly. To allow direct comparison between different types of frequency estimators, a method is introduced to evaluate the quality and robustness of estimators for a common signal space covering a wide range of realistic parameter combinations. The method is illustrated using three different mean frequency estimators: (1) a first order autoregressive estimator in combination with a stationary echo filter; (2) a second order autoregressive estimator; and (3) a complex linear regression estimator in combination with a stationary echo filter. It is concluded that, for the parameter combinations considered, the complex linear regression estimator exhibits the best quality (low variance and bias of the estimate) and robustness (consistent quality for all parameter combinations).

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14

LUI, Kenneth Wing Kin, and Hing Cheung SO. "Time Delay Estimator Based on Frequency Estimation Approach." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E93-A, no. 6 (2010): 1248–50. http://dx.doi.org/10.1587/transfun.e93.a.1248.

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15

Kim, B. K., and J. A. Bossi. "Wind shear estimation by frequency-shaped optimal estimator." Journal of Guidance, Control, and Dynamics 9, no. 2 (March 1986): 164–68. http://dx.doi.org/10.2514/3.20085.

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16

Liu Hsu, R. Ortega, and G. Damm. "A globally convergent frequency estimator." IEEE Transactions on Automatic Control 44, no. 4 (April 1999): 698–713. http://dx.doi.org/10.1109/9.754808.

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17

Daeyoung Kim, M. J. Narasimha, and D. C. Cox. "An improved single frequency estimator." IEEE Signal Processing Letters 3, no. 7 (July 1996): 212–14. http://dx.doi.org/10.1109/97.508168.

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18

Kam, Pooi Yuen. "FSK detection using frequency estimator." Electronics Letters 31, no. 19 (September 14, 1995): 1631–32. http://dx.doi.org/10.1049/el:19951154.

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19

Milczarek, Hubert, Czesław Leśnik, Igor Djurović, and Adam Kawalec. "Estimating the Instantaneous Frequency of Linear and Nonlinear Frequency Modulated Radar Signals—A Comparative Study." Sensors 21, no. 8 (April 17, 2021): 2840. http://dx.doi.org/10.3390/s21082840.

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Automatic modulation recognition plays a vital role in electronic warfare. Modern electronic intelligence and electronic support measures systems are able to automatically distinguish the modulation type of an intercepted radar signal by means of real-time intra-pulse analysis. This extra information can facilitate deinterleaving process as well as be utilized in early warning systems or give better insight into the performance of hostile radars. Existing modulation recognition algorithms usually extract signal features from one of the rudimentary waveform characteristics, namely instantaneous frequency (IF). Currently, there are a small number of studies concerning IF estimation methods, specifically for radar signals, whereas estimator accuracy may adversely affect the performance of the whole classification process. In this paper, five popular methods of evaluating the IF–law of frequency modulated radar signals are compared. The considered algorithms incorporate the two most prevalent estimation techniques, i.e., phase finite differences and time-frequency representations. The novel approach based on the generalized quasi-maximum likelihood (QML) method is also proposed. The results of simulation experiments show that the proposed QML estimator is significantly more accurate than the other considered techniques. Furthermore, for the first time in the publicly available literature, multipath influence on IF estimates has been investigated.

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20

Chen, Jian Qiu, and Ping Tan. "Identification of Frequency Response Function of Shaking Table with a New Estimator." Applied Mechanics and Materials 44-47 (December 2010): 719–23. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.719.

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Shaking table is a nonlinear system, which is a more nonlinear system with payload. System can be usually as linear system nearby working point in control strategy. 1 H -estimator or 2 H -estimator is used for identifying the Frequency Response Function (FRF) of the system. 1 H -estimator is a lower-estimator and 2 H -estimator is an over-estimator, both have large estimating errors. In this paper, a new estimator, m H -estimator, is used for the identification of the shaking table system’s FRF, and whose parameters are estimated by differential evolution (DE) which makes m H closed to the true FRF H . This control strategy can reduce the steps of iterative learning control (ILC) of shaking table system, and the affection of payload characteristic.

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21

Perron, Pierre, and Serena Ng. "AN AUTOREGRESSIVE SPECTRAL DENSITY ESTIMATOR AT FREQUENCY ZERO FOR NONSTATIONARITY TESTS." Econometric Theory 14, no. 5 (October 1998): 560–603. http://dx.doi.org/10.1017/s0266466698145024.

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Many unit root and cointegration tests require an estimate of the spectral density function at frequency zero of some process. Commonly used are kernel estimators based on weighted sums of autocovariances constructed using estimated residuals from an AR(1) regression. However, it is known that with substantially correlated errors, the OLS estimate of the AR(1) parameter is severely biased. In this paper, we first show that this least-squares bias induces a significant increase in the bias and mean-squared error (MSE) of kernel-based estimators. We then consider a variant of the autoregressive spectral density estimator that does not share these shortcomings because it bypasses the use of the estimate from the AR(1) regression. Simulations and local asymptotic analyses show its bias and MSE to be much smaller than those of a kernel-based estimator when there is strong negative serial correlation. We also include a discussion about the appropriate choice of the truncation lag.

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22

Lewandowski, Michał, and Janusz Walczak. "Current spectrum estimation using Prony's estimator and coherent resampling." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 33, no. 3 (April 29, 2014): 989–97. http://dx.doi.org/10.1108/compel-01-2013-0013.

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Purpose – A highly accurate method of current spectrum estimation of a nonlinear load is presented in this paper. Using the method makes it possible to evaluate the current injection frequency domain model of a nonlinear load from previously recorded time domain voltage and current waveforms. The paper aims to discuss these issues. Design/methodology/approach – The method incorporates the idea of coherent resampling (resampling synchronously with the base frequency of the signal) followed by the discrete Fourier transform (DFT) to obtain the frequency spectrum. When DFT is applied to a synchronously resampled signal, the spectrum is free of negative DFT effects (the spectrum leakage, for example). However, to resample the signal correctly it is necessary to know its base frequency with high accuracy. To estimate the base frequency, the first-order Prony's frequency estimator was used. Findings – It has been shown that the presented method may lead to superior results in comparison with window interpolated Fourier transform and time-domain quasi-synchronous sampling algorithms. Research limitations/implications – The method was designed for steady-state analysis in the frequency domain. The voltage and current waveforms across load terminals should be recorded simultaneously to allow correct voltage/current phase shift estimation. Practical implications – The proposed method can be used in case when the frequency domain model of a nonlinear load is desired and the voltage and current waveforms recorded across load terminals are available. The method leads to correct results even when the voltage/current sampling frequency has not been synchronized with the base frequency of the signal. It can be used for off-line frequency model estimation as well as in real-time DSP systems to restore coherent sampling of the analysed signals. Originality/value – The method proposed in the paper allows to estimate a nonlinear load frequency domain model from current and voltage waveforms with higher accuracy than other competitive methods, while at the same time its simplicity and computational efficiency is retained.

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23

Ricci, Stefano, and Valentino Meacci. "FPGA-Based Doppler Frequency Estimator for Real-Time Velocimetry." Electronics 9, no. 3 (March 8, 2020): 456. http://dx.doi.org/10.3390/electronics9030456.

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In range-Doppler ultrasound applications, the velocity of a target can be measured by transmitting a mechanical wave, and by evaluating the Doppler shift present on the received echo. Unfortunately, detecting the Doppler shift from the received Doppler spectrum is not a trivial task, and several complex estimators, with different features and performance, have been proposed in the literature for achieving this goal. In several real-time applications, hundreds of thousands of velocity estimates must be produced per second, and not all of the proposed estimators are capable of performing at these high rates. In these challenging conditions, the most widely used approaches are the full centroid frequency estimate or the simple localization of the position of the spectrum peak. The first is more accurate, but the latter features a very quick and straightforward implementation. In this work, we propose an alternative Doppler frequency estimator that merges the advantages of the aforementioned approaches. It exploits the spectrum peak to get an approximate position of the Doppler frequency. Then, centered in this position, a centroid search is applied on a reduced frequency interval to refine the estimate. Doppler simulations are used to compare the accuracy and precision performance of the proposed algorithm with respect to current state of the art approaches. Finally, a Field Programmable Gate Array (FPGA) implementation is proposed that is capable of producing more than 200 k low noise estimates per second, which is suitable for the most demanding real-time applications.

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24

Chen, Qiushi, Xin Zhang, Qiang Yang, Lei Ye, and Mengxiao Zhao. "Performance Bound for Joint Multiple Parameter Target Estimation in Sparse Stepped-Frequency Radar: A Comparison Analysis." Sensors 19, no. 9 (April 29, 2019): 2002. http://dx.doi.org/10.3390/s19092002.

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A performance bound—Cramér-Rao lower bound (CRLB) for target estimation and detection in sparse stepped frequency radars is presented. The vector formulation of this CRLB is used to obtain a lower bound on the estimation error. The estimation performance can be transformed into different types of CRLB structures. Therefore, the expressions of bounds under three equivalent models are derived separately: time delay and Doppler stretch estimator, joint multiple parameter estimator, and sparse-based estimator. The variables to be estimated include the variances of unknown noise, range, velocity, and the real and imaginary parts of the amplitude. A general performance expression is proposed by considering the echo of the target in the line-of-sight. When the relationship between CRLB and various parameters are discussed in detail, the specific effect of waveform parameters on a single CRLB is compared and analyzed. Numerical simulations demonstrated that the resulting CRLB exhibits considerable theoretical and practical significance for the selection of optimal waveform parameters.

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25

Wang, Guangtao, Gao Cong, Ying Zhang, Zhen Hai, and Jieping Ye. "A Synopsis Based Approach for Itemset Frequency Estimation over Massive Multi-Transaction Stream." ACM Transactions on Knowledge Discovery from Data 16, no. 2 (July 21, 2021): 1–30. http://dx.doi.org/10.1145/3465238.

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The streams where multiple transactions are associated with the same key are prevalent in practice, e.g., a customer has multiple shopping records arriving at different time. Itemset frequency estimation on such streams is very challenging since sampling based methods, such as the popularly used reservoir sampling, cannot be used. In this article, we propose a novel k -Minimum Value (KMV) synopsis based method to estimate the frequency of itemsets over multi-transaction streams. First, we extract the KMV synopses for each item from the stream. Then, we propose a novel estimator to estimate the frequency of an itemset over the KMV synopses. Comparing to the existing estimator, our method is not only more accurate and efficient to calculate but also follows the downward-closure property. These properties enable the incorporation of our new estimator with existing frequent itemset mining (FIM) algorithm (e.g., FP-Growth) to mine frequent itemsets over multi-transaction streams. To demonstrate this, we implement a KMV synopsis based FIM algorithm by integrating our estimator into existing FIM algorithms, and we prove it is capable of guaranteeing the accuracy of FIM with a bounded size of KMV synopsis. Experimental results on massive streams show our estimator can significantly improve on the accuracy for both estimating itemset frequency and FIM compared to the existing estimators.

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26

Siewert, Thilo M., Matthias Schmidt-Rubart, and Dominik J. Schwarz. "Cosmic radio dipole: Estimators and frequency dependence." Astronomy & Astrophysics 653 (September 2021): A9. http://dx.doi.org/10.1051/0004-6361/202039840.

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The cosmic radio dipole is of fundamental interest to studies of cosmology. Recent works have put forth open questions about the nature of the observed cosmic radio dipole. In the current work, we use simulated source count maps to test a linear and a quadratic estimator for possible biases in the estimated dipole amplitude with respect to the masking procedure. We find a superiority on the part of the quadratic estimator, which we used to analyse the TGSS-ADR1, WENSS, SUMSS, and NVSS radio source catalogues, spread over a decade of frequencies. We applied the same masking strategy to all four surveys to produce comparable results. In order to address the differences in the observed dipole amplitudes, we cross-matched the two surveys located at both ends of the analysed frequency range. For the linear estimator, we identified a general bias in the estimated dipole directions. The positional offsets of the quadratic estimator to the cosmic microwave background (CMB) dipole for skies with 107 simulated sources is found to be below one degree and the absolute accuracy of the estimated dipole amplitudes is better than 10−3. For the four radio source catalogues, we find an increasing dipole amplitude with decreasing frequency, which is consistent with results from the literature and the results of the cross-matched catalogue. We conclude that for all analysed surveys, the observed cosmic radio dipole amplitudes exceed the expectations derived from the CMB dipole, which cannot strictly be explained by a kinematic dipole alone.

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27

Ibrahim, Nur Farahiah, Zahari Abu Bakar, and Azlina Idris. "Improving Space-Time-Frequency MIMO-OFDM with ICI Self-Cancellation Scheme using Least Square Error Estimator." Scientific Research Journal 12, no. 1 (June 1, 2015): 25. http://dx.doi.org/10.24191/srj.v12i1.5436.

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Channel estimation techniques for Multiple-input Multiple-output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) based on comb type pilot arrangement with least-square error (LSE) estimator was investigated with space-time-frequency (STF) diversity implementation. The frequency offset in OFDM effected its performance. This was mitigated with the implementation of the presented inter-carrier interference self-cancellation (ICI-SC) techniques and different space-time subcarrier mapping. STF block coding in the system exploits the spatial, temporal and frequency diversity to improve performance. Estimated channel was fed into a decoder which combined the STF decoding together with the estimated channel coefficients using LSE estimator for equalization. The performance of the system was compared by measuring the symbol error rate with a PSK-16 and PSK-32. The results show that subcarrier mapping together with ICI-SC were able to increase the system performance. Introduction of channel estimation was also able to estimate the channel coefficient at only 5dB difference with a perfectly known channel.

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28

Luo, Li, Guido H. Bruck, and Peter Jung. "Musical Fundamental Frequency Estimator Based on Harmonic Pattern Match." International Journal of Semantic Computing 09, no. 02 (June 2015): 261–79. http://dx.doi.org/10.1142/s1793351x15400061.

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The fundamental frequency (F0) plays a vital role in music signal analysis and processing. However, due to non-stationary noise, undesired physical vibration from the musical instruments, the robust estimation of F0 remains a main challenge. In this paper, a F0 estimation algorithm of music signals based on harmonic pattern match (HPM) is described to achieve more reliable estimation accuracy. The algorithm utilizes the autocorrelation both in the time domain and in the frequency domain, exploiting the spectrum subset to guide the search of F0 candidates (FCs), and an efficient mechanism to evaluate the match between each FC and the harmonic pattern of the input signal. The harmonic pattern of the measured spectrum is presented by sub-pitch in each segmented sub-band. Finally, the estimated $$ \hat{F}_0$$ is selected to best match the sub-pitches under a weighting strategy. Evaluation experiments were performed over a musical instruments database consisting of single pitched notes and the viability of the HPM algorithm was demonstrated to be competitive with several other F0 estimators.

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29

Stanković, Ljubiša, Miloš Daković, and Thayananthan Thayaparan. "A real-time time-frequency based instantaneous frequency estimator." Signal Processing 93, no. 5 (May 2013): 1392–97. http://dx.doi.org/10.1016/j.sigpro.2012.11.005.

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30

Brands, Peter J., Arnold P. G. Hoeks, and Léon A. F. Ledoux. "A Single Bit RF Domain Complex Cross-Correlation Velocity Estimator for Color Flow Mapping." Ultrasonic Imaging 19, no. 3 (July 1997): 180–94. http://dx.doi.org/10.1177/016173469701900302.

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This paper evaluates the performance of a one bit mean frequency estimator to estimate blood flow velocity for ultrasound color flow mapping. This one bit mean frequency estimator, referred to as BC3 estimator, is derived from the recently introduced complex cross-correlation model (C3M) employing the full dynamic data range. The C3M velocity estimator is not suitable for application in color flow mapping because of its high hardware complexity and associated computational load. The BC3 estimator estimates the mean blood flow velocity using only two complex cross-correlation coefficients. For this purpose the latter are computed by means of a complex one bit cross-correlation operation. Each sample of the RF signals is converted into an one bit representation based on the sign of the real and imaginary part of the RF samples. A full derivation and mathematical description of the BC3 estimator is presented. In addition, a thorough performance evaluation of the BC3 estimator in comparison with the full dynamic range C3M velocity estimator is carried out by means of signal simulations to document the effect of signal to noise ratio, sample frequency and bandwidth. For the specific simulation conditions considered the standard deviation of both estimators (C3M and BC3) is comparable. The bias of the BC3 estimator appears to be a function of velocity, while the full dynamic range C3M velocity estimator exhibits no bias. The simulation results are confirmed by evaluation of data from an in vivo measurement. Taking into account the low hardware complexity and computational load in combination with the achieved precision, it may be concluded that the BC3 estimator is well suited for implementation in color flow mapping.

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31

Djurovic, Igor. "A WD-RANSAC Instantaneous Frequency Estimator." IEEE Signal Processing Letters 23, no. 5 (May 2016): 757–61. http://dx.doi.org/10.1109/lsp.2016.2551732.

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32

Kay, S., and R. Nekovei. "An efficient two-dimensional frequency estimator." IEEE Transactions on Acoustics, Speech, and Signal Processing 38, no. 10 (1990): 1807–9. http://dx.doi.org/10.1109/29.60114.

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33

Tavares, G. N., L. M. Tavares, and A. Petrolino. "An Improved Feedforward Frequency Offset Estimator." IEEE Transactions on Signal Processing 56, no. 5 (May 2008): 2155–60. http://dx.doi.org/10.1109/tsp.2007.913166.

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34

Aboutanios, E. "A Modified Dichotomous Search Frequency Estimator." IEEE Signal Processing Letters 11, no. 2 (February 2004): 186–88. http://dx.doi.org/10.1109/lsp.2003.821676.

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Xiao, Y. C., P. Wei, X. C. Xiao, and H. M. Tai. "Fast and accurate single frequency estimator." Electronics Letters 40, no. 14 (2004): 910. http://dx.doi.org/10.1049/el:20040558.

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36

Amador, Sandro, Mahmoud El-Kafafy, Álvaro Cunha, and Rune Brincker. "A New Maximum Likelihood Estimator Formulated in Pole-Residue Modal Model." Applied Sciences 9, no. 15 (August 1, 2019): 3120. http://dx.doi.org/10.3390/app9153120.

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Recently, a lot of efforts have been devoted to developing more precise Modal Parameter Estimation (MPE) techniques. This is explained by the necessity in civil, mechanical and aerospace engineering of obtaining accurate estimates for the modal parameters of the tested structures, as well as of determining reliable confidence intervals for these estimates. The Non-linear Least Squares (NLS) identification techniques based on Maximum Likelihood (ML) have been increasingly used in modal analysis to improve precision of estimates provided by the Least Squares (LS) based estimators when they are not accurate enough. Apart from providing more accurate estimates, the main advantage of the ML estimators, with regard to their LS counterparts, is that they allow for taking into account not only the measured Frequency Response Functions (FRFs) but also the noise information during the parametric identification process and, therefore, provide the modal parameters estimates together with their uncertainties bounds. In this paper, a new derivation of a Maximum Likelihood Estimator formulated in Pole-residue Modal Model (MLE-PMM) is presented. The proposed formulation is meant to be used in combination with the Least Squares Frequency Domain (LSCF) to improve the precision of the modal parameter estimates and compute their confidence intervals. Aiming at demonstrating the efficiency of the proposed approach, it is applied to two simulated examples in the final part of the paper.

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Li, Qi, and Jeffrey S. Racine. "SMOOTH VARYING-COEFFICIENT ESTIMATION AND INFERENCE FOR QUALITATIVE AND QUANTITATIVE DATA." Econometric Theory 26, no. 6 (March 17, 2010): 1607–37. http://dx.doi.org/10.1017/s0266466609990739.

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We propose a semiparametric varying-coefficient estimator that admits both qualitative and quantitative covariates along with a test for correct specification of parametric varying-coefficient models. The proposed estimator is exceedingly flexible and has a wide range of potential applications including hierarchical (mixed) settings, small area estimation, etc. A data-driven cross-validatory bandwidth selection method is proposed that can handle both the qualitative and quantitative covariates and that can also handle the presence of potentially irrelevant covariates, each of which can result in finite-sample efficiency gains relative to the conventional frequency (sample-splitting) estimator that is often found in such settings. Theoretical underpinnings including rates of convergence and asymptotic normality are provided. Monte Carlo simulations are undertaken to assess the proposed estimator’s finite-sample performance relative to the conventional semiparametric frequency estimator and to assess the finite-sample performance of the proposed test for correct parametric specification.

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Saucier, Antoine, Matthew Marchant, and Michel Chouteau. "A fast and accurate frequency estimation method for canceling harmonic noise in geophysical records." GEOPHYSICS 71, no. 1 (January 2006): V7—V18. http://dx.doi.org/10.1190/1.2159063.

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The cancellation of harmonic noise from geophysical records can be achieved by subtracting an estimate of the harmonic noise. Estimating the harmonic noise consists of estimating the fundamental frequency and the amplitudes and phases of all harmonics. We propose a new frequency-estimation method that builds upon the estimator originally proposed by Nyman and Gaiser. This Nyman and Gaiser estimation (NGE) method exploits the fact that the noise fundamental frequency is known to be close to 60 Hz. The NGE method is based on solving a system of four equations that determine the amplitude, phase, and frequency of a given harmonic in the harmonic noise. Hence, NGE can produce frequency estimates for all harmonics. Our improved estimator uses a suitable linear combination of these NGE frequency estimates to produce a more accurate estimate of the fundamental frequency. Our method is more accurate than NGE, and its accuracy is comparable to least-squares estimation (LSE). The advantage of our method is that it is about two times faster than LSE. This speed gain can become valuable when processing large magnetotelluric (MT) data records. Applying our method to the restoration of MT data, we found that the harmonic noise amplitude in the periodogram is reduced by at least 60 dB to a level below that of MT data.

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Su, Ing Jiunn, and Chiao Chan Huang. "Robust MVDR Estimation via Polynomial Rooting for MC-CDMA Carrier Frequency Offset." Applied Mechanics and Materials 284-287 (January 2013): 2687–93. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.2687.

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In this letter, we present a blind carrier frequency offset (CFO) estimator by exploiting the polynomial rooting technique for multicarrier-code division multiple access (MC-CDMA) systems. Relative high accuracy and low-complexity to the CFO estimation can be achieved by rooting a polynomial. Simulation results are provided for illustrating the effectiveness of the proposed blind polynomial rooting estimator.

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Koike, Yuta. "De-Biased Graphical Lasso for High-Frequency Data." Entropy 22, no. 4 (April 17, 2020): 456. http://dx.doi.org/10.3390/e22040456.

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This paper develops a new statistical inference theory for the precision matrix of high-frequency data in a high-dimensional setting. The focus is not only on point estimation but also on interval estimation and hypothesis testing for entries of the precision matrix. To accomplish this purpose, we establish an abstract asymptotic theory for the weighted graphical Lasso and its de-biased version without specifying the form of the initial covariance estimator. We also extend the scope of the theory to the case that a known factor structure is present in the data. The developed theory is applied to the concrete situation where we can use the realized covariance matrix as the initial covariance estimator, and we obtain a feasible asymptotic distribution theory to construct (simultaneous) confidence intervals and (multiple) testing procedures for entries of the precision matrix.

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Abdullah, Muhammad, Tahir N. Malik, Ali Ahmed, Muhammad F. Nadeem, Irfan A. Khan, and Rui Bo. "A Novel Hybrid GWO-LS Estimator for Harmonic Estimation Problem in Time Varying Noisy Environment." Energies 14, no. 9 (May 1, 2021): 2587. http://dx.doi.org/10.3390/en14092587.

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The power quality of the Electrical Power System (EPS) is greatly affected by electrical harmonics. Hence, accurate and proper estimation of electrical harmonics is essential to design appropriate filters for mitigation of harmonics and their associated effects on the power quality of EPS. This paper presents a novel statistical (Least Square) and meta-heuristic (Grey wolf optimizer) based hybrid technique for accurate detection and estimation of electrical harmonics with minimum computational time. The non-linear part (phase and frequency) of harmonics is estimated using GWO, while the linear part (amplitude) is estimated using the LS method. Furthermore, harmonics having transients are also estimated using proposed harmonic estimators. The effectiveness of the proposed harmonic estimator is evaluated using various case studies. Comparing the proposed approach with other harmonic estimation techniques demonstrates that it has a minimum mean square error with less complexity and better computational efficiency.

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Zhang, Junwei, Qiuyan Zhang, Bo Zhang, and Chao Ding. "Prior Rough Estimation and Trigonometric Decomposition Method for Frequency Estimation of Sine Wave Distorted by Noise." Fluctuation and Noise Letters 18, no. 01 (January 9, 2019): 1950008. http://dx.doi.org/10.1142/s0219477519500081.

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Frequency estimation of sine wave distorted by noise plays an important role in modern power systems. The widely used windowed interpolation fast Fourier transform (WIFFT) can only reduce, but not completely remove, the estimation errors at the cost of an increment in the computation burden of frequency estimation. This paper proposes a frequency estimator by using the prior rough frequency estimation and trigonometric decomposition under white Gaussian noises. Firstly, a prior rough frequency is estimated by the three spectral lines WIFFT based on the Hanning window. Then, a parallel iteration of trigonometric decomposition is applied for the accurate frequency estimation. The gradient-descent method is used in the parallel iteration procedure, which can effectively restrain the interferences from harmonics and noise. Simulation and experiments results show that the proposed method can achieve accurate frequency estimation within one cycle.

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Won, YongJu, JongGyu Oh, JinSeop Lee, and JoonTae Kim. "A Study of an Iterative Channel Estimation Scheme of FS-FBMC System." Wireless Communications and Mobile Computing 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6784142.

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A filter bank multicarrier on offset-quadrature amplitude modulation (FBMC/OQAM) system is an alternative multicarrier modulation scheme that does not need cyclic prefix (CP) even in the presence of a multipath fading channel by the properties of prototype filter. The FBMC/OQAM system can be implemented either by using the poly-phase network with fast fourier transform (PPN-FFT) or by using the extended FFT on a frequency-spreading (FS) domain. In this paper, we propose an iterative channel estimation scheme for each sub channel of a FBMC/OQAM system over a frequency-spreading domain. The proposed scheme first estimates the channel using the received pilot signal in the subchannel domain and interpolates the estimated channel to fine frequency-spreading domain. Then the channel compensated FS domain pilot is despread again to modify the channel state information (CSI) estimation. Computer simulation shows that the proposed method outperforms the conventional FBMC/OQAM channel estimator in a frequency selective channel.

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44

Viglione, A. "Confidence intervals for the coefficient of L-variation in hydrological applications." Hydrology and Earth System Sciences 14, no. 11 (November 11, 2010): 2229–42. http://dx.doi.org/10.5194/hess-14-2229-2010.

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Abstract. The coefficient of L-variation (L-CV) is commonly used in statistical hydrology, in particular in regional frequency analysis, as a measure of steepness for the frequency curve of the hydrological variable of interest. As opposed to the point estimation of the L-CV, in this work we are interested in the estimation of the interval of values (confidence interval) in which the L-CV is included at a given level of probability (confidence level). Several candidate distributions are compared in terms of their suitability to provide valid estimators of confidence intervals for the population L-CV. Monte-Carlo simulations of synthetic samples from distributions frequently used in hydrology are used as a basis for the comparison. The best estimator proves to be provided by the log-Student t distribution whose parameters are estimated without any assumption on the underlying parent distribution of the hydrological variable of interest. This estimator is shown to also outperform the non parametric bias-corrected and accelerated bootstrap method. An illustrative example of how this result can be used in hydrology is presented, namely in the comparison of methods for regional flood frequency analysis. In particular, it is shown that the confidence intervals for the L-CV can be used to assess the amount of spatial heterogeneity of flood data not explained by regionalization models.

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Nielsen, Jesper Kjær, Tobias Lindstrøm Jensen, Jesper Rindom Jensen, Mads Græsbøll Christensen, and Søren Holdt Jensen. "Fast fundamental frequency estimation: Making a statistically efficient estimator computationally efficient." Signal Processing 135 (June 2017): 188–97. http://dx.doi.org/10.1016/j.sigpro.2017.01.011.

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Xiaohu Liang, Aijun Liu, Xiaofei Pan, Qingshuang Zhang, and Feng Chen. "A New and Accurate Estimator With Analytical Expression for Frequency Estimation." IEEE Communications Letters 20, no. 1 (January 2016): 105–8. http://dx.doi.org/10.1109/lcomm.2015.2496149.

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47

Kay, S. "A fast and accurate single frequency estimator." IEEE Transactions on Acoustics, Speech, and Signal Processing 37, no. 12 (1989): 1987–90. http://dx.doi.org/10.1109/29.45547.

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48

YOU, Y. H. "A Simplified Autocorrelation-Based Single Frequency Estimator." IEICE Transactions on Communications E89-B, no. 7 (July 1, 2006): 2096–98. http://dx.doi.org/10.1093/ietcom/e89-b.7.2096.

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49

Atoui, Sakina, Noureddine Doghmane, and Saddek Afifi. "Blind frequency offset estimator for OFDM systems." TELKOMNIKA (Telecommunication Computing Electronics and Control) 17, no. 6 (December 1, 2019): 2722. http://dx.doi.org/10.12928/telkomnika.v17i6.11957.

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50

Pálfi, V., and B. Renczes. "A novel frequency domain periodic signal estimator." Journal of Physics: Conference Series 1065 (August 2018): 052037. http://dx.doi.org/10.1088/1742-6596/1065/5/052037.

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