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Estimation of chirp signals with time-varying amplitudes

Meng, Xiangxia; Jakobsson, Andreas LU ; Li, Xiukun and Lei, Yahui (2018) In Signal Processing 147. p.1-10
Abstract

The problem of parameters estimation of signals composed of an unknown number of chirps with time-varying amplitude is presented using a sparse reconstruction framework. The method employs a parametric model using a weighted combination of splines to model the time-varying nature of the signal amplitudes. To obtain high-resolution of the frequencies and to avoid large dimensional matrices, a dictionary refinement technique is employed. The method can accurately estimate the amplitude and frequency parameters of multiple signal components, and may be extended to allow for non-linear chirps. Furthermore, an efficient implementation to solve the resulting optimization problem is proposed. Results on both synthetic and experimental signals... (More)

The problem of parameters estimation of signals composed of an unknown number of chirps with time-varying amplitude is presented using a sparse reconstruction framework. The method employs a parametric model using a weighted combination of splines to model the time-varying nature of the signal amplitudes. To obtain high-resolution of the frequencies and to avoid large dimensional matrices, a dictionary refinement technique is employed. The method can accurately estimate the amplitude and frequency parameters of multiple signal components, and may be extended to allow for non-linear chirps. Furthermore, an efficient implementation to solve the resulting optimization problem is proposed. Results on both synthetic and experimental signals illustrate the efficient performance of the algorithm.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
ADMM, Chirp signals, Sparse reconstruction, Time-varying amplitude
in
Signal Processing
volume
147
pages
10 pages
publisher
Elsevier
external identifiers
  • scopus:85041490413
ISSN
0165-1684
DOI
10.1016/j.sigpro.2018.01.017
language
English
LU publication?
yes
id
74829d11-7048-42aa-a2c8-dab4a2062b6b
date added to LUP
2018-02-20 08:28:15
date last changed
2018-05-29 11:02:02
@article{74829d11-7048-42aa-a2c8-dab4a2062b6b,
  abstract     = {<p>The problem of parameters estimation of signals composed of an unknown number of chirps with time-varying amplitude is presented using a sparse reconstruction framework. The method employs a parametric model using a weighted combination of splines to model the time-varying nature of the signal amplitudes. To obtain high-resolution of the frequencies and to avoid large dimensional matrices, a dictionary refinement technique is employed. The method can accurately estimate the amplitude and frequency parameters of multiple signal components, and may be extended to allow for non-linear chirps. Furthermore, an efficient implementation to solve the resulting optimization problem is proposed. Results on both synthetic and experimental signals illustrate the efficient performance of the algorithm.</p>},
  author       = {Meng, Xiangxia and Jakobsson, Andreas and Li, Xiukun and Lei, Yahui},
  issn         = {0165-1684},
  keyword      = {ADMM,Chirp signals,Sparse reconstruction,Time-varying amplitude},
  language     = {eng},
  month        = {06},
  pages        = {1--10},
  publisher    = {Elsevier},
  series       = {Signal Processing},
  title        = {Estimation of chirp signals with time-varying amplitudes},
  url          = {http://dx.doi.org/10.1016/j.sigpro.2018.01.017},
  volume       = {147},
  year         = {2018},
}