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Proceedings ArticleDOI

Exact maximum likelihood estimation of superimposed exponential signals in noise

Yoram Bresler, +1 more
- Vol. 10, pp 1824-1827
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TLDR
A unified framework for the exact Maximum Likelihood estimation of the parameters of superimposed exponential signals in noise, encompassing both the single and the multiexperiment cases, is presented.
Abstract
A unified framework for the exact Maximum Likelihood estimation of the parameters of superimposed exponential signals in noise, encompassing both the single and the multiexperiment cases (respectively the time series and the array problems), is presented. An exact expression for the ML criterion is derived in terms of the prediction polynomial of the noiseless signal, and an iterative algorithm for the maximization of this criterion is presented. A simulation example shows the estimator to be capable of providing more accurate frequency estimates than currently existing techniques.

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Citations
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Journal ArticleDOI

Exact maximum likelihood parameter estimation of superimposed exponential signals in noise

TL;DR: A unified framework for the exact maximum likelihood estimation of the parameters of superimposed exponential signals in noise, encompassing both the time series and the array problems, is presented and the present formulation is used to interpret previous methods.
Proceedings ArticleDOI

Estimation of Signal Parameters via Rotational Invariance Techniques - ESPRIT

TL;DR: Though ESPRIT is discussed in the context of direction-of-arrival estimation, it can be applied to a wide variety of problems including spectral estimation and has several advantages over earlier techniques such as MUSIC including improved performance, reduced computational load, freedom from array characterization/calibration, and reduced sensitivity to array perturbations.
Proceedings ArticleDOI

Genetic algorithms for maximum likelihood parameter estimation

TL;DR: The authors introduce genetic algorithms to solve the exact maximum-likelihood equations arising in a typical signal processing problem, and it is shown that it can outperform existing high-performance parameter estimation algorithms under difficult conditions.
Journal ArticleDOI

Robust parametric transfer function estimation using complex logarithmic frequency response data

TL;DR: Besides its robustness to lack of prior noise information, it is demonstrated that the logarithmic estimator behaves remarkably well in the presence of outliers.
Proceedings ArticleDOI

Comparative performance of ESPRIT and MUSIC for direction-of-arrival estimation

TL;DR: Results of computer simulations carried out to compare the resolution and error performance of ESPRIT and the well-known conventional MUSIC algorithm are presented.
References
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Journal ArticleDOI

The differentiation of pseudoinverses and nonlinear least squares problems whose variables separate.

TL;DR: Algorithms are presented which make extensive use of well-known reliable linear least squares techniques, and numerical results and comparisons are given.
Journal ArticleDOI

The Retrieval of Harmonics from a Covariance Function

TL;DR: In this paper, a new method for retrieving harmonics from a covariance function is introduced, based on a theorem of Caratheodory about the trigonometrical moment problem.

The differentiation of pseudo-inverses and non-linear least squares problems whose variables separate.

G. H. GOLUBf, +1 more
TL;DR: In this paper, the least square fit of nonlinear models of the form {(0t, Yi), l,, m, qgj, ti, and the modified functional r2( 0t (lY O(0 t)/(0)yl)22) is considered.
Journal ArticleDOI

Estimation of frequencies of multiple sinusoids: Making linear prediction perform like maximum likelihood

TL;DR: In this paper, the frequency estimation performance of the forward-backward linear prediction (FBLP) method was improved for short data records and low signal-to-noise ratio (SNR) by using information about the rank M of the signal correlation matrix.
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