What question did this study set out to answer?

To derive optimal theoretical bounds and computationally feasible maximum-likelihood estimation algorithms for single-frequency complex tones observed in discrete time with additive noise.

September 1, 1974IEEE Transactions on Information Theory

Single tone parameter estimation from discrete-time observations

View Full Paper

Ask AI

Bookmark

Authors

DRD. C. RifeARUP Laboratories (United States)RBR. BoorstynCore Competence

Discussion

Member takes

Implication

Theoretical analysis reveals efficient maximum-likelihood algorithms for single-tone estimation in noisy discrete signals, highlighting practical implementations via the discrete Fourier transform.

Key Points

To derive optimal theoretical bounds and computationally feasible maximum-likelihood estimation algorithms for single-frequency complex tones observed in discrete time with additive noise.
Derived analytical Cramér-Rao lower bounds and maximum-likelihood (ML) estimators for tone frequency, amplitude, and phase.
Connected the ML estimation formulation directly to the discrete Fourier transform (DFT) to create practical digital algorithms.
Simulated algorithm performance to evaluate threshold effects and verify theoretical error bounds.
Established exact Cramér-Rao bounds and proved key structural properties of the resulting maximum-likelihood estimators.
Demonstrated that DFT-based approximations reliably capture maximum-likelihood performance down to defined signal-to-noise threshold levels.

Cite This Study

Rife et al. (1974) studied this question.

synapsesocial.com/papers/6a107cf0d478ddac0ffd0a56 https://doi.org/10.1109/tit.1974.1055282

View Full Paper

Ask AI

Bookmark

September 1, 1974IEEE Transactions on Information Theory

Single tone parameter estimation from discrete-time observations

View Full Paper

Ask AI

Bookmark

Authors

DRD. C. RifeARUP Laboratories (United States)RBR. BoorstynCore Competence

Discussion

Member takes

Implication

Theoretical analysis reveals efficient maximum-likelihood algorithms for single-tone estimation in noisy discrete signals, highlighting practical implementations via the discrete Fourier transform.

Key Points

To derive optimal theoretical bounds and computationally feasible maximum-likelihood estimation algorithms for single-frequency complex tones observed in discrete time with additive noise.
Derived analytical Cramér-Rao lower bounds and maximum-likelihood (ML) estimators for tone frequency, amplitude, and phase.
Connected the ML estimation formulation directly to the discrete Fourier transform (DFT) to create practical digital algorithms.
Simulated algorithm performance to evaluate threshold effects and verify theoretical error bounds.
Established exact Cramér-Rao bounds and proved key structural properties of the resulting maximum-likelihood estimators.
Demonstrated that DFT-based approximations reliably capture maximum-likelihood performance down to defined signal-to-noise threshold levels.

Cite This Study

Rife et al. (1974) studied this question.

synapsesocial.com/papers/6a107cf0d478ddac0ffd0a56 https://doi.org/10.1109/tit.1974.1055282

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark