Time-Frequency Energy Distributions Meet Compressed Sensing - ENS de Lyon - École normale supérieure de Lyon Access content directly
Journal Articles IEEE Transactions on Signal Processing Year : 2010

Time-Frequency Energy Distributions Meet Compressed Sensing

Patrick Flandrin
  • Function : Author
  • PersonId : 839765
Pierre Borgnat
  • Function : Author
  • PersonId : 838021

Abstract

In the case of multicomponent signals with amplitude and frequency modulations, the idealized representation which consists of weighted trajectories on the time-frequency (TF) plane, is intrinsically sparse. Recent advances in optimal recovery from sparsity constraints thus suggest to revisit the issue of TF localization by exploiting sparsity, as adapted to the specific context of (quadratic) TF distributions. Based on classical results in TF analysis, it is argued that the relevant information is mostly concentrated in a restricted subset of Fourier coefficients of the Wigner-Ville distribution neighbouring the origin of the ambiguity plane. Using this incomplete information as the primary constraint, the desired distribution follows as the minimum l1-norm solution in the transformed TF domain. Possibilities and limitations of the approach are demonstrated via controlled numerical experiments, its performance is assessed in various configurations and the results are compared with standard techniques. It is shown that improved representations can be obtained, though at a computational cost which is significantly increased.
Fichier principal
Vignette du fichier
IEEE_TSP_TFCS_double.pdf (308.82 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

ensl-00475930 , version 1 (23-04-2010)

Identifiers

Cite

Patrick Flandrin, Pierre Borgnat. Time-Frequency Energy Distributions Meet Compressed Sensing. IEEE Transactions on Signal Processing, 2010, 58 (6), pp.2974-2982. ⟨10.1109/TSP.2010.2044839⟩. ⟨ensl-00475930⟩
245 View
1387 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More