Parameter estimation and signal reconstruction

DOI10.1007/s10092-021-00431-8OpenAlexW3197630679MaRDI QIDQ2230559

Michael-Ralf Skrzipek

Publication date: 24 September 2021

Published in: Calcolo (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10092-021-00431-8

zbMATH Keywords

recurrence relationoverdetermined systemexponential sumChebyshev polynomialHessenberg matrixSzegő polynomialrecovery of structured functionsprony polynomialprony's method

Mathematics Subject Classification ID

Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Numerical computation of roots of polynomial equations (65H04)

Cites Work

• Parameter estimation for nonincreasing exponential sums by Prony-like methods
• Applications of Szegö polynomials to digital signal processing
• Numerical methods for roots of polynomials. Part I
• Parameter estimation for exponential sums by approximate prony method
• Asymptotics for zeros of Szegő polynomials associated with trigonometric polynomial signals
• Polynomial evaluation and associated polynomials
• Szegő polynomials applied to frequency analysis
• An algorithm for locating all zeros of a real polynomial
• Numerical methods for roots of polynomials. II
• Signal recovery by discrete approximation and a Prony-like method
• Continuation methods for the computation of zeros of Szegő polynomials
• Szegö polynomials associated with Wiener-Levinson filters
• Nonlinear approximation by sums of nonincreasing exponentials
• Matrix pencil method for estimating parameters of exponentially damped/undamped sinusoids in noise
• Core-Chasing Algorithms for the Eigenvalue Problem
• Sampling Moments and Reconstructing Signals of Finite Rate of Innovation: Shannon Meets Strang–Fix
• Sampling Schemes for Multidimensional Signals With Finite Rate of Innovation
• On Recovery of Sparse Signals Via $\ell _{1}$ Minimization
• Sparse Signal Reconstruction: LASSO and Cardinality Approaches
• Sampling signals with finite rate of innovation
• Roots of Polynomials Expressed in Terms of Orthogonal Polynomials
• For most large underdetermined systems of linear equations the minimal 𝓁₁‐norm solution is also the sparsest solution
• Norms for Smoothing and Estimation
• Polynomial zerofinders based on Szegő polynomials
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