Title :
A stochastic multisample extension of Morris´s robust detector in bounded amplitude noise
Author_Institution :
Dept. of Math., Lowell Univ., MA
fDate :
9/1/1988 12:00:00 AM
Abstract :
A Bayesian approach to the problem of finite sample detection of a signal in an unknown noise environment is formulated. A mathematical solution is given for the minimax (robust) test for detecting the presence of a stochastic signal of known prior probability in unknown additive noise of bounded magnitude. This solution remains valid for a constant signal with identical components when the additive noises are independent. This extends results of J.M. Morris (ibid., vol.IT-62, p.199-209, March 1980). Worst-case performance bounds for detecting the presence of a pattern class are derived
Keywords :
Bayes methods; signal detection; stochastic processes; Bayesian approach; Morris´s robust detector; bounded amplitude noise; constant signal; finite sample detection; stochastic multisample extension; unknown noise environment; Additive noise; Bayesian methods; Detectors; Minimax techniques; Noise robustness; Signal detection; Stochastic processes; Stochastic resonance; Testing; Working environment noise;
Journal_Title :
Information Theory, IEEE Transactions on
