A theoretical framework for problems requiring robust behavior

Author

Carrillo, Rafael E. ; Aysal, Tuncer C. ; Barner, Kenneth E.

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Delaware, Newark, DE, USA

fYear

2009

fDate

13-16 Dec. 2009

Firstpage

25

Lastpage

28

Abstract

This paper develops a generalized Cauchy density (GCD) based theoretical approach that allows the formulation of challenging problems in a robust fashion. The proposed framework subsumes the generalized Gaussian distribution (GGD) family based developments, thereby guaranteeing performance improvements over traditional problem formulation techniques. This robust framework can be adapted to a variety of applications in signal processing. We formulate two particular applications under this framework in this paper: (1) Robust reconstruction methods for compressed sensing and (2) robust estimation in sensor networks with noisy channels.

Keywords

Gaussian channels; Gaussian distribution; Gaussian noise; estimation theory; signal reconstruction; GCD; GGD; channel noise; generalized Cauchy density; generalized Gaussian distribution; robust estimation; robust reconstruction methods; sensor networks; signal processing; Compressed sensing; Gaussian distribution; Maximum likelihood estimation; Noise robustness; Probability distribution; Reconstruction algorithms; Robust stability; Signal processing; Statistics; Yield estimation; Maximum likelihood estimation; compressed sensing; impulsive noise; nonlinear filtering and estimation; sensor networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2009 3rd IEEE International Workshop on

Conference_Location

Aruba, Dutch Antilles

Print_ISBN

978-1-4244-5179-1

Electronic_ISBN

978-1-4244-5180-7

Type

conf

DOI

10.1109/CAMSAP.2009.5413284

Filename

5413284