Title :
Signal detection under mismatch (Corresp.)
Author :
Kazakos, Dimitri
fDate :
7/1/1982 12:00:00 AM
Abstract :
A binary detection problem of the Neymann-Pearson type, in which the probability density functions used are inaccurate versions of the true ones, are considered. The performance of the above suboptimal detection scheme as the number of observations increases is investigated. A necessary and sufficient condition is given for the exponential convergence to zero of the two error probabilities as the number of observations increases. The condition is in terms of an inequality between differences of asymptotic per sample informational divergence expressions.
Keywords :
Signal detection; Equations; Estimation theory; Frequency domain analysis; Gaussian noise; Gaussian processes; Marine vehicles; Pattern recognition; Probability density function; Random processes; Signal detection;
Journal_Title :
Information Theory, IEEE Transactions on
DOI :
10.1109/TIT.1982.1056520