Title :
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
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;
Conference_Titel :
Digital Signal Processing Workshop, 1994., 1994 Sixth IEEE
Conference_Location :
Yosemite National Park, CA
Print_ISBN :
0-7803-1948-6
DOI :
10.1109/DSP.1994.379864
