Physio-associative temporal sensor integration

Erik P. Blasch; James C. Gainey Jr.

doi:10.1117/12.304834

25 March 1998 Physio-associative temporal sensor integration

Erik P. Blasch, James C. Gainey Jr.

Proceedings Volume 3390, Applications and Science of Computational Intelligence; (1998) https://doi.org/10.1117/12.304834
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

The paper describes the physio-associative temporal sensors integration algorithm which is motivated by the observed function of the thalamus and utilizes signals theory mathematics to model how a human efficiently perceives information in the environment. The algorithm is consistent with that of an aircraft pilot; namely, to estimate, filter, and predict sensed afferent signals and produce efferent controls under dynamic flight conditions. Dynamic sensor integration under uncertainty requires feature selection which can be formulated as an associative-learning problem in which sensed states are represented as current situational beliefs, and the information either excites or inhibits long-term memory associations. The objective of the learner/observer is to (1) abstract salient signals from the environment, (2) integrate the signal for real-time beliefs, and (3) compare beliefs to learned associations. Biologically, the paper models these processes from the biological systems of the eye, thalamus, and association-cortex; respectively. By selecting the optimal set of mutually non-exclusive sensors and comparing the integrated signal to learned associations, the physio-associative temporal algorithm maximizes the identification of targets in a simulated dynamic flight situation.

Citation Download Citation

Erik P. Blasch and James C. Gainey Jr. "Physio-associative temporal sensor integration", Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); https://doi.org/10.1117/12.304834

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