Robust multiple classification of known signals in additive noise-an asymptotic weak signal approach

DOI10.1109/18.212288zbMath0782.62095OpenAlexW2157853385MaRDI QIDQ4202082

Moncef Mettiji, Ola G. Hössjer

Publication date: 12 October 1993

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/1aea22e6ab050aac134085540fc5b5c182e9f531

zbMATH Keywords

Monte Carlo simulationsPitman efficacyminimax theorymaximal correlationasymptotic error probabilitiesshortest distancelimiting error probabilityadditive noise model\(R\)-type distancescorrelation-based detectorsdistance-based detectorsfinite sample size error probabilitiesM-type distancesweak signal approach

Mathematics Subject Classification ID

Robustness and adaptive procedures (parametric inference) (62F35) Estimation and detection in stochastic control theory (93E10) Survival analysis and censored data (62N99) Inference from stochastic processes (62M99) Communication, information (94A99) Statistical aspects of information-theoretic topics (62B10)

Related Items (2)

Asymptotically stable detection of a weak signal ⋮ Asymptotically Stable Tests with Application to Robust Detection

This page was built for publication: Robust multiple classification of known signals in additive noise-an asymptotic weak signal approach