Linear prediction approach to oversampling parameter estimation for multiple complex sinusoids

Zhou, Zhenhua; So, Hing Cheung

doi:10.1016/j.sigpro.2011.12.003

Cited by 8 publications

(7 citation statements)

References 23 publications

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“…We derive the CRLB under the assumption that q i is complex-valued white Gaussian noise with the variance σ 2 . Under this assumption, the log-likelihood function of the observed signal x i is expressed as: 6) and the Fisher information matrix (FIM) of x i is [41]:…”

Section: Discussionmentioning

confidence: 99%

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Parametric Modeling for Two-Dimensional Harmonic Signals With Missing Harmonics

et al. 2019

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The problem of parametric modeling for the two-dimensional (2-D) harmonic signals with missing harmonics is addressed. The modeling process consists of two parts. First, we devise the joint spatialtemporal linearly constrained minimum variance beamformer and perform the joint estimation of the spatial and temporal fundamental frequencies for each pitch of the signal based on the maximum harmonic model. Second, we differentiate the competing signal models by the number of harmonic components and derive the maximum a posteriori criterion for the 2-D harmonic signals. As a result, the harmonic components of each pitch are detected according to their spectral powers. The simulation and experimental results are provided to show the superiority of the proposed signal modeling methodology over other existing schemes. INDEX TERMS Fundamental frequency estimation, harmonic detection, two-dimensional harmonic signal, linearly constrained minimum variance beamformer, maximum a posteriori criterion, maximum harmonic model.

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Section: Discussionmentioning

confidence: 99%

“…For the parameter estimation, the fundamental frequencies are of most interest. Once their estimates are obtained, the remaining linear parameters such as amplitudes and initial phases, can be computed as a linear least-squares (LLS) solution [6].…”

Section: Introductionmentioning

confidence: 99%

Parametric Modeling for Two-Dimensional Harmonic Signals With Missing Harmonics

et al. 2019

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“…First of all, the following LP equation is established, which is based on the LP property of the sinusoidal signals [8][9][10]:…”

Section: Sinusoidal Parameter Estimation With the Rwlpmentioning

confidence: 99%

“…It is known that the maximum likelihood estimator (MLE) is statistically optimal [4], whereas it requires enormous computational cost in the multi-dimensional search. To lower the computational complexity, several kinds of computationally efficient techniques have been developed for the parameter estimation, such as the subspace-based algorithms [5][6][7] and the linear prediction (LP)-based methods [8][9][10]. However, in most of the above work, the background noise is assumed as white Gaussian.…”

Section: Introductionmentioning

confidence: 99%

Linear Prediction Approach to the Robust Parameter Estimation for the Damped Sinusoids

Zhou

Liu²,

Christensen

et al. 2020

2020 15th IEEE International Conference on Signal Processing (ICSP)

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In this paper, we focus on the problem of parameter estimation for the damped sinusoids, which are corrupted by impulsive noise. To provide a robust initial guess for the current parameter estimators, the robust weighted linear prediction (RWLP) estimator is developed, where the parameter estimates are obtained by minimizing the weighted p -norm of the linear prediction (LP) error vector. The Markov optimum weighting matrix is derived, and an iteratively reweighted least-squares (IRLS) procedure is devised to calculate the LP coefficient estimates. The simulation results demonstrate the robustness of the RWLP estimator by comparing with the 2 -norm based counterpart, and the computational efficiency by comparing with the p -MUSIC algorithm.

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“…Apart from the standard one-dimensional signal model [4]- [6], multi-dimensional spectral estimation [7] in fact has many applications such as array processing [8]- [9], nuclear magnetic resonance (NMR) spectroscopy [10], wireless communication channel estimation [11]- [12] as well as detection and localization of multiple targets using multiple-input multiple-output (MIMO) radar [13].…”

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confidence: 99%

Correlation-based algorithm for multi-dimensional single-tone frequency estimation

Sun

Lin

2013

Signal Processing

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In this paper, parameter estimation for a R-dimensional (R-D) single cisoid with R ≥ 2 in additive white Gaussian noise is addressed. By exploiting the correlation of the data samples, we construct R single-tone sequences which contain the R-D frequency parameters. Based on linear prediction and weighted linear squares techniques, two proposals are developed for fast and accurate frequency estimation from each constructed sequence. The two devised estimators are proved to be asymptotically unbiased while their variances achieve Cramér-Rao lower bound when the signal-to-noise ratio and/or data length tend to infinity. Computer simulations are also included to compare the proposed approach with conventional R-D harmonic retrieval schemes in terms of mean square error performance and computational complexity.

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Linear prediction approach to oversampling parameter estimation for multiple complex sinusoids

Cited by 8 publications

References 23 publications

Parametric Modeling for Two-Dimensional Harmonic Signals With Missing Harmonics

Parametric Modeling for Two-Dimensional Harmonic Signals With Missing Harmonics

Linear Prediction Approach to the Robust Parameter Estimation for the Damped Sinusoids

Correlation-based algorithm for multi-dimensional single-tone frequency estimation

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