Problems of identification

Abstract

Several (I in number) observers cooperate to identify the member of a population of items (J in number) transmitting. The observers' measurements of basic item-identifying (known) parameters are error-prone; errors are frequently non-Gaussian (gross-error-prone). Various methods are introduced and studied for combining the observers; observations so as to achieve as high a probability of correct identification as possible. Linear combinations of observations are shown to be less than satisfactory in the environment described. Continuation and generalization of the work is in progress. Keywords: robust estimation in a restricted parameter space; Cauchy and contaminated normal measurement errors; maximum likelihood and Bayes estimates; the discrimination problem