A new algorithm for iterative deconvolution of sparse spike trains

Author

Goussard, Y. ; Demoment, G. ; Idier, J.

Author_Institution

Ecole Superieure d´´Electricite, Gif-sur-Yvette, France

fYear

1990

fDate

3-6 Apr 1990

Firstpage

1547

Abstract

An iterative algorithm for deconvolution of Bernoulli-Gaussian processes is presented. This detection-estimation problem is formulated as that of a change of initial conditions in linear least-squares estimation. An algorithm with a very simple structure is obtained. It allows the evaluation of either marginal or joint likelihood criteria without any approximation; the resulting method is easy to implement and computationally inexpensive and remains nearly optimal

Keywords

iterative methods; random processes; signal processing; Bernoulli-Gaussian processes; detection-estimation problem; initial conditions; iterative algorithm; iterative deconvolution; joint likelihood criteria; linear least-squares estimation; marginal criteria; signal processing; sparse spike trains; Acoustic distortion; Acoustic noise; Additive noise; Change detection algorithms; Deconvolution; Iterative algorithms; Linear systems; Reflectivity; Signal processing; Uninterruptible power systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on

Conference_Location

Albuquerque, NM

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.1990.115707

Filename

115707

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2886894