Random Distortion Testing and applications

Author

Pastor, Dominique ; Quang-Thang Nguyen

Author_Institution

Inst. Telecom, Univ. Eur. de Bretagne, Brest, France

fYear

2013

Firstpage

6347

Lastpage

6351

Abstract

We address Random Distortion Testing (RDT), that is, the problem of testing whether the Mahalanobis distance between a random signal Θ and a known deterministic model θ₀ exceeds some given τ ≥ 0 or not, when Θ has unknown probability distribution and is observed in additive independent Gaussian noise with positive definite covariance matrix. A suitable optimality criterion for RDT is presented and theoretical results on optimal tests for this criterion are given. Several applications of these results are presented and analyzed. They address the detection of signals in case of model mismatch and the detection of deviations from model θ₀.

Keywords

Gaussian noise; covariance matrices; signal detection; Mahalanobis distance; additive independent Gaussian noise; deterministic model; event testing; hypothesis testing; positive definite covariance matrix; probability distribution; random distortion testing; random signal; signal detection; Covariance matrices; Distortion; Signal to noise ratio; Standards; Testing; Tin; Event testing; Mahalanobis norm; hypothesis testing; invariance; random distortion testing; test with maximal constant conditional power;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on

Conference_Location

Vancouver, BC

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2013.6638887

Filename

6638887