Skip to Main content Skip to Navigation
Conference papers

A multi-parameter optimization approach for complex continuous sparse modelling

Abstract : The main focus of this work is the estimation of a complex valued signal assumed to have a sparse representation in an uncountable dictionary of signals. The dictionary elements are parameterized by a real-valued vector and the available observations are corrupted with an additive noise. By applying a linearization technique, the original model is recast as a constrained sparse perturbed model. The problem of the computation of the involved multiple parameters is addressed from a nonconvex optimization viewpoint. A cost function is defined including an arbitrary Lipschitz differentiable data fidelity term accounting for the noise statistics, and an l0-like penalty. A proximal algorithm is then employed to solve the resulting nonconvex and nonsmooth minimization problem. Experimental results illustrate the good practical performance of the proposed approach when applied to 2D spectrum analysis.
Complete list of metadatas

Cited literature [9 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01077331
Contributor : Emilie Chouzenoux <>
Submitted on : Friday, October 24, 2014 - 2:44:19 PM
Last modification on : Wednesday, February 26, 2020 - 7:06:06 PM
Document(s) archivé(s) le : Friday, April 14, 2017 - 12:08:44 PM

File

DSP2014.pdf
Files produced by the author(s)

Identifiers

Citation

Emilie Chouzenoux, Jean-Christophe Pesquet, Anisia Florescu. A multi-parameter optimization approach for complex continuous sparse modelling. 19th International Conference on Digital Signal Processing (DSP 2014), Aug 2014, Hong-Kong, China. pp.817 - 820, ⟨10.1109/ICDSP.2014.6900780⟩. ⟨hal-01077331⟩

Share

Metrics

Record views

338

Files downloads

266