The Experts below are selected from a list of 146361 Experts worldwide ranked by ideXlab platform

Hassan Foroosh - One of the best experts on this subject based on the ideXlab platform.

  • Frequency Estimation of sinusoids from nonuniform samples
    Signal Processing, 2016
    Co-Authors: Alam Abbas Syed, Qiyu Sun, Hassan Foroosh
    Abstract:

    Sinusoid signals with multiple frequencies appear in various systems and their frequencies may carry some important features. Frequency Estimation from their discrete samples is one of the fundamental problems and many Frequency estimators have been proposed for uniform sampling setting. In this paper, Frequency estimators based on adaptive notch filtering are proposed for nonuniform sampling setting. We observe that some dynamic systems associated with adaptive notch filters can be solved in nonuniformly sampled time steps with high accuracy. This leads us to propose a digital adaptive notch filtering method to estimate Frequency of a sinusoidal signal with single Frequency from its nonuniform samples. The proposed method exhibits convergent and robust Frequency Estimation in the presence of random sampling noises, and its variance is comparable to the Cramer-Rao lower bound in the presence of additive white noise. The above method designed for single Frequency Estimation could track abrupt single Frequency change of an input signal, but it is not applicable directly for multiple Frequency Estimation. Our simulations show that the proposed estimators have robust performance for sinusoidal signals with multiple distinct frequencies, and they can be used to separate two very close frequencies of an input signal in a highly noisy sampling environment. HighlightsThe method exhibits convergent and robust Estimation (CRLB lower bound).The continuous dynamic system discretized with high accuracy.Well designed to handle seamlessly both uniform and nonuniform sampled data.Comparable performance with state of the art discrete adaptive notch filter.Cascade and parallel methods to handle multiple frequencies.

Yanlin Shen - One of the best experts on this subject based on the ideXlab platform.

  • phase correction autocorrelation based Frequency Estimation method for sinusoidal signal
    Signal Processing, 2017
    Co-Authors: Yaqing Tu, Yanlin Shen
    Abstract:

    To improve the precision of Frequency Estimation, a phase correction autocorrelation-based Frequency Estimation method for sinusoidal signal is proposed. Firstly, a phase correction autocorrelation is developed to reduce the effect of non-half period sampling signal on autocorrelation. Secondly, reference signal is generated according to phase correction autocorrelation signal. Finally, an error function between phase correction autocorrelation signal and reference signal is constructed and Frequency Estimation is obtained by calculating the minimum of error function. To demonstrate the superiority of the proposed method, computational complexity is analyzed, simulations and experiments are performed. Theoretical analysis and simulations demonstrate that the proposed method reduces the influence of non- half period sampling signal and has better Frequency Estimation performance than the interpolated DFT method, the modified covariance method for correlation, the two-stage autocorrelation method and the expanded autocorrelation method. The measurement experiments of LFMCW radars validate the effectiveness and superiority of the proposed method in practice. Phase correction autocorrelation is devised to correct the phase and reduce bias.Proposed method reduces the effect of sinusoidal signal's non-half period sampling.Proposed method is computationally efficient.

  • A phase match based Frequency Estimation method for sinusoidal signals
    The Review of scientific instruments, 2015
    Co-Authors: Yanlin Shen, Lin-jun Chen, Ting-ao Shen
    Abstract:

    Accurate Frequency Estimation affects the ranging precision of linear Frequency modulated continuous wave (LFMCW) radars significantly. To improve the ranging precision of LFMCW radars, a phase match based Frequency Estimation method is proposed. To obtain Frequency Estimation, linear prediction property, autocorrelation, and cross correlation of sinusoidal signals are utilized. The analysis of computational complex shows that the computational load of the proposed method is smaller than those of two-stage autocorrelation (TSA) and maximum likelihood. Simulations and field experiments are performed to validate the proposed method, and the results demonstrate the proposed method has better performance in terms of Frequency Estimation precision than methods of Pisarenko harmonic decomposition, modified covariance, and TSA, which contribute to improving the precision of LFMCW radars effectively.

Alam Abbas Syed - One of the best experts on this subject based on the ideXlab platform.

  • Frequency Estimation of sinusoids from nonuniform samples
    Signal Processing, 2016
    Co-Authors: Alam Abbas Syed, Qiyu Sun, Hassan Foroosh
    Abstract:

    Sinusoid signals with multiple frequencies appear in various systems and their frequencies may carry some important features. Frequency Estimation from their discrete samples is one of the fundamental problems and many Frequency estimators have been proposed for uniform sampling setting. In this paper, Frequency estimators based on adaptive notch filtering are proposed for nonuniform sampling setting. We observe that some dynamic systems associated with adaptive notch filters can be solved in nonuniformly sampled time steps with high accuracy. This leads us to propose a digital adaptive notch filtering method to estimate Frequency of a sinusoidal signal with single Frequency from its nonuniform samples. The proposed method exhibits convergent and robust Frequency Estimation in the presence of random sampling noises, and its variance is comparable to the Cramer-Rao lower bound in the presence of additive white noise. The above method designed for single Frequency Estimation could track abrupt single Frequency change of an input signal, but it is not applicable directly for multiple Frequency Estimation. Our simulations show that the proposed estimators have robust performance for sinusoidal signals with multiple distinct frequencies, and they can be used to separate two very close frequencies of an input signal in a highly noisy sampling environment. HighlightsThe method exhibits convergent and robust Estimation (CRLB lower bound).The continuous dynamic system discretized with high accuracy.Well designed to handle seamlessly both uniform and nonuniform sampled data.Comparable performance with state of the art discrete adaptive notch filter.Cascade and parallel methods to handle multiple frequencies.

Kimberly L. Turner - One of the best experts on this subject based on the ideXlab platform.

  • Precise Frequency Estimation in a microelectromechanical parametric resonator
    Applied Physics Letters, 2007
    Co-Authors: Michael V. Requa, Kimberly L. Turner
    Abstract:

    The authors report here on precise resonant Frequency Estimation using the nonlinear spectral features of parametric resonance. Demonstration of 100 parts per 109 Frequency resolution at room temperature is accompanied by a technique to observe the phase trajectories of escape in bistable parametrically resonant systems. The system offers an expanded dynamic range over similar linear resonators. Precise Frequency Estimation has implications in resonant mass sensing.

Zhang Qing-guo - One of the best experts on this subject based on the ideXlab platform.

  • The Rife Frequency Estimation algorithm Based on real-time FFT
    Signal Processing, 2009
    Co-Authors: Zhang Qing-guo
    Abstract:

    A new amendment Rife Frequency Estimation algorithm based on real-time amendment FFT is proposed,which is derived from the interpolation algorithm of FFT Frequency estimated algorithm.The theoretical calculation process and related error formula are given out.The Frequency Estimation algorithms verifying and comparing are carried on the ADSP-TS201 platform after Monte Carlo simulation experiment.It is shown that the accuracy of the Frequency Estimation has been improved in the setting Frequency range without increasing the calculation amount obviously by using the new algorithm,and the MSE is close to the Cramer-Rao Lower Bound (CRLB).So it has some practical value for engineering.