A universal methodology for signal classification in non-Gaussian environments

Author

Warke, N. ; Orsak, G.C.

Author_Institution

Dept. of Electr. & Comput. Eng., George Mason Univ., Fairfax, VA, USA

fYear

1994

fDate

2-5 Oct 1994

Firstpage

101

Lastpage

104

Abstract

The signal classification problem is posed as an M-ary hypothesis testing problem. We develop an asymptotically optimal universal classifier which does not depend on the true statistical model of the environment. We show that the relevant error probabilities decay at least exponentially in the length of the data vector. To support these results we present simulation results comparing the performance of the proposed universal detector with that of a matched filter receiver for finite test sequences

Keywords

error statistics; exponential distribution; signal detection; signal processing; M-ary hypothesis testing problem; asymptotically optimal universal classifier; data vector length; error probabilities; exponential decay; finite test sequences; matched filter receiver; non-Gaussian environments; performance; signal classification; simulation results; Detectors; Electronic mail; Error probability; Gaussian noise; Maximum likelihood detection; Noise robustness; Pattern classification; Statistics; Testing; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Signal Processing Workshop, 1994., 1994 Sixth IEEE

Conference_Location

Yosemite National Park, CA

Print_ISBN

0-7803-1948-6

Type

conf

DOI

10.1109/DSP.1994.379864

Filename

379864