Separation of system dynamics and line spectra via sparse representation

Lipeng Ning; Tryphon T. Georgiou; Allen Tannenbaum

doi:10.1109/CDC.2010.5717810

Separation of system dynamics and line spectra via sparse representation

Lipeng Ning, Tryphon T. Georgiou, Allen Tannenbaum

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

The purpose of this work is to explore the use of sparse representation in the context of system identification and, in particular, to address the important problem of identifying spectral lines in the presence of colored noise. To this end we formulate an ℓ₁ optimization problem, similar to Lasso, to incorporate an AR-model that accounts for the noise-color dynamics with contribution of sinusoids drawn out of an over-complete basis (dictionary). A suitable generalization for correlated time series sharing common components is also considered.

Original language	English (US)
Title of host publication	2010 49th IEEE Conference on Decision and Control, CDC 2010
Pages	473-478
Number of pages	6
DOIs	https://doi.org/10.1109/CDC.2010.5717810
State	Published - Dec 1 2010
Event	2010 49th IEEE Conference on Decision and Control, CDC 2010 - Atlanta, GA, United States Duration: Dec 15 2010 → Dec 17 2010

Other

Other	2010 49th IEEE Conference on Decision and Control, CDC 2010
Country	United States
City	Atlanta, GA
Period	12/15/10 → 12/17/10

Access

10.1109/CDC.2010.5717810

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abstract = "The purpose of this work is to explore the use of sparse representation in the context of system identification and, in particular, to address the important problem of identifying spectral lines in the presence of colored noise. To this end we formulate an ℓ1 optimization problem, similar to Lasso, to incorporate an AR-model that accounts for the noise-color dynamics with contribution of sinusoids drawn out of an over-complete basis (dictionary). A suitable generalization for correlated time series sharing common components is also considered.",

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AU - Tannenbaum, Allen

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AB - The purpose of this work is to explore the use of sparse representation in the context of system identification and, in particular, to address the important problem of identifying spectral lines in the presence of colored noise. To this end we formulate an ℓ1 optimization problem, similar to Lasso, to incorporate an AR-model that accounts for the noise-color dynamics with contribution of sinusoids drawn out of an over-complete basis (dictionary). A suitable generalization for correlated time series sharing common components is also considered.

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