A robust approach to sequential detection

The problem of signal detection in severe and/or changing noise environments, often encountered in underwater acoustics, radar, and some communications applications, is considered. The detector operates at near optimum levels for a particular noise environment, and is robust by virtue of maintaining high efficiency in other than nominal noise environments by adapting its optimum nonlinearity using an m-interval polynomial approximation (MIPA). It is shown that the MIPA detectors have the same basic structure for fixed sample size and variable sample size, i.e. sequential operation. The sequential MIPA detectors are asymptotically optimum, increase their transmission rate up to four times as compared to their fixed-sample size counterparts, and are highly insensitive to variations of noise compared to detectors based on min-max theory. The estimation and updating of the detector parameters can be accomplished using parallel processors operating in a recursive mode without disturbing the decision process