Joint estimation of time delay and frequency delay in impulsive noise using fractional lower order statistics

Ma, Xinyu; Nikias, C.L.

doi:10.1109/78.542175

Cited by 179 publications

(16 citation statements)

References 13 publications

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“…Since the probability density functions is not always expressed in a closed form, there are some challenges to overcome the analytic difficulties. Thus, there have been constructed a variety of methods: the approximate maximum likelihood estimation [14,15], quantiles method [16,17], fractional lower order moment method [18,19], method of logcumulant [20], the logarithmic moment method [21] and more. Unfortunately, some of those methods cannot be applied due to computational problems associated with limited range of estimation, restricted range of parameters, high computational costs, or requiring large number of data.…”

Section: Methods For Estimation Of Stable Law Parametersmentioning

confidence: 99%

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Levy’s stable distribution for stock crash detecting

et al. 2019

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In this paper we study the possibility of construction indicators-precursors relying on one of the most power-law tailed distributions - Levy’s stable distribution. Here, we apply Levy’s parameters for 29 stock indices for the period from 1 March 2000 to 28 March 2019 daily values and show their effectiveness as indicators of crisis states on the example of Dow Jones Industrial Average index for the period from 2 January 1920 to 2019. In spite of popularity of the Gaussian distribution in financial modeling, we demonstrated that Levy’s stable distribution is more suitable due to its theoretical reasons and analysis results. And finally, we conclude that stability α and skewness β parameters of Levy’s stable distribution which demonstrate characteristic behavior for crash and critical states, can serve as an indicator-precursors of unstable states.

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Section: Methods For Estimation Of Stable Law Parametersmentioning

confidence: 99%

“…from equation (5). With equation (17), we can derive (18) and (19) around k = 0 the parameters (α, β, γ, δ) can be estimated. In case when (γ, δ) are far from the standard value of (1, 0), each parameter can not be estimated accurately.…”

Section: Estimation Of Levy's Stable Distributionmentioning

confidence: 99%

Levy’s stable distribution for stock crash detecting

et al. 2019

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“…This assumption is found in generalized crosscorrelation (GCC) techniques, and their different frequency domain weighting such as phase transform (GCC-PHAT) [3], Roth weighting (GCC-ROTH), and smoothed coherence factor weighting (GCC-SCOT). As a result, these classical methods experience severe degradation in the spatial resolution [4] when the Gaussian assumption does not work.…”

Section: Introductionmentioning

confidence: 99%

“…The SαS has heavier tails than the Gaussian distribution, giving a much better approximation to real-world audio signals [1], [2]. Although an approach based on fractional lower order statistics (FLOS) available in α-stable noises is proposed [4], this method relies on the estimating of the characteristic exponent of the model.…”

Section: Introductionmentioning

confidence: 99%

Robust time delay estimation based on non-Gaussian impulsive acoustic channel

Pena¹,

Lima²,

Sousa³

et al. 2020

JCIS

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The aim of this letter is to propose a new robust method for time delay estimation over impulsive noise and investigate its practical implications. The method uses a nonlinear data transformation associated with the generalized crosscorrelation technique. Hence, simulations and experiments show better performance than traditional methods without increasing the computational cost. Our practical experiments indicate the possibility of obtaining a correlated impulsive noise, in which the proposed method is still presenting accurate estimations.

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“…For robust TDE, Ma and Nikias introduced [16] the following p l -norm cost function about the delay D and the attenuation factor β : …”

Section: Lp-norm Minimization-based Estimationmentioning

confidence: 99%

Joint Noise Reduction and lp-Norm Minimization for Enhancing Time Delay Estimation in Colored Noise

Tu¹,

Xia²

2016

JCC

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Time delay estimation (TDE) is an important issue in signal processing. Conventional TDE algorithms are usually efficient under white noise environments. In this paper, a joint noise reduction and p l -norm minimization method is presented to enhance TDE in colored noise. An improved subspace method for colored noise reduction is first performed. Then the time delay is estimated by using an p l -norm minimization method. Because the clean speech signal form the noisy signal is well extracted by noise reduction and the p l -norm minimization method is robust, the TDE accuracy can be enhanced. Experiment results confirm that the proposed joint estimation method obtains more accurate TDE than several conventional algorithms in colored noise, especially in the case of low signal-to-noise ratio.

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Joint estimation of time delay and frequency delay in impulsive noise using fractional lower order statistics

Cited by 179 publications

References 13 publications

Levy’s stable distribution for stock crash detecting

Levy’s stable distribution for stock crash detecting

Robust time delay estimation based on non-Gaussian impulsive acoustic channel

Joint Noise Reduction and lp-Norm Minimization for Enhancing Time Delay Estimation in Colored Noise

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