The Experts below are selected from a list of 282 Experts worldwide ranked by ideXlab platform

Pooi Yuen Kam - One of the best experts on this subject based on the ideXlab platform.

  • Maximum-Likelihood, Magnitude-Based, Amplitude and Noise Variance Estimation
    IEEE Signal Processing Letters, 2021
    Co-Authors: Yan Jin, Tianyu Song, Pooi Yuen Kam
    Abstract:

    Maximum likelihood (ML) amplitude and noise variance estimation without having to jointly estimate the frequency and the phase and based only on information from the noisy received signal magnitude, is studied for a Single Sinusoid in complex additive white Gaussian noise. This estimation problem is equivalent to the classic problem of parameter estimation for the Rician distribution. While solving the likelihood equation is impossible in general, we propose a new approach based on a large argument approximation. For the case with known noise variance, a closed-form ML amplitude estimator is obtained, which outperforms the conventional root-mean-square estimator. For the case with unknown noise variance, the closed-form joint amplitude and noise variance estimators obtained do not require prior knowledge of one another.

  • on threshold snr in estimating the frequency and phase of a noisy Single Sinusoid
    Vehicular Technology Conference, 2014
    Co-Authors: Pooi Yuen Kam
    Abstract:

    The paper investigates the signal-to-noise ratio (SNR) threshold effect in estimating the frequency and phase of a Single Sinusoid over the additive white Gaussian noise. This is done by making use of the results on the time-domain, phase-based estimator and the additive observation phase noise (AOPN) models developed in [8]. Specifically, the relationship between the threshold SNR and the similarity/dissimilarity of the Tikhonov distribution model of the AOPN to the corresponding Gaussian approximation is studied from three different but relevant aspects, namely, the series convergence, the Kullback-Leibler (KL) divergence and the AOPN variance. The interconnection that exists among these three aspects is revealed. In comparison to the mean-square-error-based approach, the AOPN-based approach proposed here has an advantage that it can lead to an analytical result on the performance of threshold SNR.

  • VTC Spring - On Threshold SNR in Estimating the Frequency and Phase of a Noisy Single Sinusoid
    2014 IEEE 79th Vehicular Technology Conference (VTC Spring), 2014
    Co-Authors: Pooi Yuen Kam
    Abstract:

    The paper investigates the signal-to-noise ratio (SNR) threshold effect in estimating the frequency and phase of a Single Sinusoid over the additive white Gaussian noise. This is done by making use of the results on the time-domain, phase-based estimator and the additive observation phase noise (AOPN) models developed in [8]. Specifically, the relationship between the threshold SNR and the similarity/dissimilarity of the Tikhonov distribution model of the AOPN to the corresponding Gaussian approximation is studied from three different but relevant aspects, namely, the series convergence, the Kullback-Leibler (KL) divergence and the AOPN variance. The interconnection that exists among these three aspects is revealed. In comparison to the mean-square-error-based approach, the AOPN-based approach proposed here has an advantage that it can lead to an analytical result on the performance of threshold SNR.

  • phase based time domain estimation of the frequency and phase of a Single Sinusoid in awgn the role and applications of the additive observation phase noise model
    IEEE Transactions on Information Theory, 2013
    Co-Authors: Pooi Yuen Kam
    Abstract:

    This paper presents the theoretical foundation for time-domain, phase-based estimation of the frequency and phase of a Single Sinusoid in additive white Gaussian noise (AWGN), analogous to the theoretical foundation provided by Rife and Boorstyn for frequency-domain, Fourier-transform-based estimation. It is shown from the maximum a posteriori probability (MAP) and the maximum likelihood (ML) estimation principles that with the additive observation phase noise (AOPN), due to the AWGN, being described by its a posteriori distribution conditioned on the received signal magnitude, the received signal phase is a sufficient statistic for estimating the Single-Sinusoid angle parameters. Using a geometric approach, the exact statistical model for the AOPN is derived, where the a posteriori probability density function (pdf) and the corresponding a priori pdf are given by explicit, closed-form expressions that are valid for arbitrary signal-to-noise ratios (SNRs). The a posteriori pdf is Tikhonov, and is of particular interest as it establishes the AOPN model for phase-based frequency/phase MAP/ML estimation in the time domain. It is further illustrated that the results derived can yield various AOPN models as special cases, and the underlying physical insights and interconnections that exist among these models are revealed. It is shown that the model derived by Tretter is an ultimate specialization in the high SNR limit of the AOPN models developed here. For high SNR, the a posteriori Tikhonov pdf can be accurately approximated by a Gaussian distribution, which leads to the best linearized AOPN model. The applications of these AOPN models to the design of linear estimators, including the linear minimum mean square error (LMMSE) estimator, the linear minimum variance estimator, and the LMMSE implementation of the weighted phase averager are presented, and their estimation performances are compared through computer simulations, with the Cramer-Rao lower bound (CRLB) and the Bayesian CRLB as the benchmark. To facilitate estimator design, the a priori statistical models of the frequency and phase are proposed from the information-theoretic perspective, and an improved phase unwrapping algorithm over that given by Fu and Kam is presented. It is shown that by incorporating all the information available in the AOPN, the estimation accuracy can be much improved.

  • Phase-Based, Time-Domain Estimation of the Frequency and Phase of a Single Sinusoid in AWGN—The Role and Applications of the Additive Observation Phase Noise Model
    IEEE Transactions on Information Theory, 2013
    Co-Authors: Pooi Yuen Kam
    Abstract:

    This paper presents the theoretical foundation for time-domain, phase-based estimation of the frequency and phase of a Single Sinusoid in additive white Gaussian noise (AWGN), analogous to the theoretical foundation provided by Rife and Boorstyn for frequency-domain, Fourier-transform-based estimation. It is shown from the maximum a posteriori probability (MAP) and the maximum likelihood (ML) estimation principles that with the additive observation phase noise (AOPN), due to the AWGN, being described by its a posteriori distribution conditioned on the received signal magnitude, the received signal phase is a sufficient statistic for estimating the Single-Sinusoid angle parameters. Using a geometric approach, the exact statistical model for the AOPN is derived, where the a posteriori probability density function (pdf) and the corresponding a priori pdf are given by explicit, closed-form expressions that are valid for arbitrary signal-to-noise ratios (SNRs). The a posteriori pdf is Tikhonov, and is of particular interest as it establishes the AOPN model for phase-based frequency/phase MAP/ML estimation in the time domain. It is further illustrated that the results derived can yield various AOPN models as special cases, and the underlying physical insights and interconnections that exist among these models are revealed. It is shown that the model derived by Tretter is an ultimate specialization in the high SNR limit of the AOPN models developed here. For high SNR, the a posteriori Tikhonov pdf can be accurately approximated by a Gaussian distribution, which leads to the best linearized AOPN model. The applications of these AOPN models to the design of linear estimators, including the linear minimum mean square error (LMMSE) estimator, the linear minimum variance estimator, and the LMMSE implementation of the weighted phase averager are presented, and their estimation performances are compared through computer simulations, with the Cramer-Rao lower bound (CRLB) and the Bayesian CRLB as the benchmark. To facilitate estimator design, the a priori statistical models of the frequency and phase are proposed from the information-theoretic perspective, and an improved phase unwrapping algorithm over that given by Fu and Kam is presented. It is shown that by incorporating all the information available in the AOPN, the estimation accuracy can be much improved.

Sam Reisenfeld - One of the best experts on this subject based on the ideXlab platform.

  • analysis of a discrete complex Sinusoid frequency estimator based on Single delay multiplication method
    International Symposium on Information Theory, 2005
    Co-Authors: Sithamparanathan Kandeepan, Sam Reisenfeld
    Abstract:

    A statistical analysis of the Single-delay multiplication based frequency estimator is treated here. The Single-delay multiplication frequency estimator for complex Single Sinusoid signals is a phase averaged estimator, which is similar to the Kay's estimator. Here we provide a study on the statistical distribution of the frequency estimates made by the estimator, and verify the analytical results using simulations. It is shown that the analytical results hold true even at very low signal to noise ratio levels. In deriving the probability density function of the frequency estimates, we explore the cross product noise terms resulting due to the multiplication operation of the estimator by making valid assumptions

  • ISIT - Analysis of a discrete complex Sinusoid frequency estimator based on Single-delay multiplication method
    Proceedings. International Symposium on Information Theory 2005. ISIT 2005., 2005
    Co-Authors: Sithamparanathan Kandeepan, Sam Reisenfeld
    Abstract:

    A statistical analysis of the Single-delay multiplication based frequency estimator is treated here. The Single-delay multiplication frequency estimator for complex Single Sinusoid signals is a phase averaged estimator, which is similar to the Kay's estimator. Here we provide a study on the statistical distribution of the frequency estimates made by the estimator, and verify the analytical results using simulations. It is shown that the analytical results hold true even at very low signal to noise ratio levels. In deriving the probability density function of the frequency estimates, we explore the cross product noise terms resulting due to the multiplication operation of the estimator by making valid assumptions

R. Vaccaro - One of the best experts on this subject based on the ideXlab platform.

  • ICASSP - The statistical performance of state-variable balancing and Prony's method in parameter estimation
    ICASSP '87. IEEE International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: Alex C. Kot, Sarangarajan Parthasarathy, D. Tufts, R. Vaccaro
    Abstract:

    This paper presents a statistical analysis of the state-variable balancing and Prony methods for estimating the parameters of exponential signals in the presence of additive noise. The case of frequency estimation for a Single Sinusoid is carried out in detail. Analytical expressions for the variances of the frequency estimates at high signal-to-noise ratios are derived. The calculated variances are compared to the Cramer-Rao bound. The results are validated by simulations over a wide range of signal-to-noise ratios. The analysis and simulations show that the state-variable balancing method can provide slightly more accurate frequency estimates while avoiding the problem of selecting the signal zeros of the Prony polynomial.

Sithamparanathan Kandeepan - One of the best experts on this subject based on the ideXlab platform.

  • analysis of a discrete complex Sinusoid frequency estimator based on Single delay multiplication method
    International Symposium on Information Theory, 2005
    Co-Authors: Sithamparanathan Kandeepan, Sam Reisenfeld
    Abstract:

    A statistical analysis of the Single-delay multiplication based frequency estimator is treated here. The Single-delay multiplication frequency estimator for complex Single Sinusoid signals is a phase averaged estimator, which is similar to the Kay's estimator. Here we provide a study on the statistical distribution of the frequency estimates made by the estimator, and verify the analytical results using simulations. It is shown that the analytical results hold true even at very low signal to noise ratio levels. In deriving the probability density function of the frequency estimates, we explore the cross product noise terms resulting due to the multiplication operation of the estimator by making valid assumptions

  • ISIT - Analysis of a discrete complex Sinusoid frequency estimator based on Single-delay multiplication method
    Proceedings. International Symposium on Information Theory 2005. ISIT 2005., 2005
    Co-Authors: Sithamparanathan Kandeepan, Sam Reisenfeld
    Abstract:

    A statistical analysis of the Single-delay multiplication based frequency estimator is treated here. The Single-delay multiplication frequency estimator for complex Single Sinusoid signals is a phase averaged estimator, which is similar to the Kay's estimator. Here we provide a study on the statistical distribution of the frequency estimates made by the estimator, and verify the analytical results using simulations. It is shown that the analytical results hold true even at very low signal to noise ratio levels. In deriving the probability density function of the frequency estimates, we explore the cross product noise terms resulting due to the multiplication operation of the estimator by making valid assumptions

Alex C. Kot - One of the best experts on this subject based on the ideXlab platform.

  • ICASSP - The statistical performance of state-variable balancing and Prony's method in parameter estimation
    ICASSP '87. IEEE International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: Alex C. Kot, Sarangarajan Parthasarathy, D. Tufts, R. Vaccaro
    Abstract:

    This paper presents a statistical analysis of the state-variable balancing and Prony methods for estimating the parameters of exponential signals in the presence of additive noise. The case of frequency estimation for a Single Sinusoid is carried out in detail. Analytical expressions for the variances of the frequency estimates at high signal-to-noise ratios are derived. The calculated variances are compared to the Cramer-Rao bound. The results are validated by simulations over a wide range of signal-to-noise ratios. The analysis and simulations show that the state-variable balancing method can provide slightly more accurate frequency estimates while avoiding the problem of selecting the signal zeros of the Prony polynomial.