Blackout detection as a multiobjective optimization problem.

Abstract

We study new fast computational procedures for a pilot blackout (total loss of vision) detection in real time. Their validity is demonstrated by data acquired during experiments with volunteer pilots on a human centrifuge. A new systematic class of very fast suboptimal group filters is employed. The utilization of various inherent group invariancies of signals involved allows us to solve the detection problem via estimation with respect to many performance criteria. The complexity of the procedures in terms of the number of computer operations required for their implementation is investigated. Various classes of such prediction procedures are investigated, analyzed and trade offs are established. Also we investigated the validity of suboptimal filtering using different group filters for different performance criteria, namely: the number of false detections, the number of missed detections, the accuracy of detection and the closeness of all procedures to a certain bench mark technique in terms of dispersion squared (mean square error). The results are compared to recent studies of detection of evoked potentials using estimation. The group filters compare favorably with conventional techniques in many cases with respect to the above mentioned criteria. Their main advantage is the fast computational processing.