Subspace-based rational interpolation of analytic functions from phase data

Authors:
Hüseyin Akçay
Affiliations:
Department of Electrical and Electronics Engineering, Anadolu University, Eskisehir, Turkey
Venue:
IEEE Transactions on Signal Processing
Year:
2010

Citing 13
Cited 3

Discrete-time signal processing

Discrete-time signal processing
A Subspace Model Identification Solution to the Identificationof Mixed Causal, Anti-Causal LTI Systems

SIAM Journal on Matrix Analysis and Applications
Continuous-time frequency domain subspace system identification

Signal Processing - Special issue: subspace methods, part II: system identification
Subspace-based identification of infinite-dimensional multivariable systems from frequency-response data

Automatica (Journal of IFAC)
A subspace algorithm for the identification of discrete time frequency domain power spectra

Automatica (Journal of IFAC)
System identification (2nd ed.): theory for the user

System identification (2nd ed.): theory for the user
Time series: data analysis and theory

Time series: data analysis and theory
A Subspace-Based Method for Solving Lagrange-Sylvester Interpolation Problems

SIAM Journal on Matrix Analysis and Applications
Noise cross PSD estimation using phase information in diffuse noise field

Signal Processing
On the uniform approximation of discrete-time systems bygeneralized Fourier series

IEEE Transactions on Signal Processing
Cross-spectrum based blind channel identification

IEEE Transactions on Signal Processing
Frequency domain subspace-based identification of discrete-time power spectra from nonuniformly spaced measurements

Automatica (Journal of IFAC)
Robust spectral factor approximation of discrete-time frequency domain power spectras

Automatica (Journal of IFAC)

Brief paper: Frequency domain subspace-based identification of discrete-time power spectra from uniformly spaced measurements

Automatica (Journal of IFAC)
Frequency domain subspace-based identification of discrete-time singular power spectra

Signal Processing
Subspace-based spectrum estimation in frequency-domain by regularized nuclear norm minimization

Signal Processing

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Abstract

In this paper, two simple subspace-based identification algorithms to identify stable linear-time-invariant systems from corrupted phase samples of frequency response function are developed. The first algorithm uses data sampled at nonuniformly spaced frequencies and is strongly consistent if corruptions are zero-mean additive random variables with a known covariance function. However, this algorithm is biased when corruptions are multiplicative, yet it exactly retrieves finite-dimensional systems from noise-free phase data using a finite amount of data. The second algorithm uses phase data sampled at equidistantly spaced frequencies and also has the same interpolation and strong consistency properties if corruptions are zero-mean additive random variables. The latter property holds also for the multiplicative noise model provided that some noise statistics are known a priori. Promising results are obtained when the algorithms are applied to simulated data.