NDLI: Value-based control of the observation-decision process

Content Provider	IEEE Xplore Digital Library
Author	McEneaney, W.M. Oran, A. Cavender, A.
Copyright Year	2008
Description	Author affiliation: Dept. of Mech. & Aerosp. Eng., Univ. of California San Diego, La Jolla, CA (McEneaney, W.M.; Oran, A.; Cavender, A.)
Abstract	Sensing in support of combat teams may be performed both by human elements and by UAVs. With the increasing pace of modern combat operations, and the developing networked-management of such, the problem of how to task sensor assets is entering the realm where automated decision-support tools should be applied. The objective by which the relative value of possible future sensor tasks should be compared is the value that they bring to the combat operation, with adjustment by the possible costs of these sensing operations. A mathematical theory allowing for the determination of the minimax value as a function of information state (where this information state is described by a probability distribution) has recently been obtained. Possible future sensing tasks are mapped into the (stochastic) observation outcomes, and these are further mapped into potential a posteriori probability distributions. The value to the combat operation is the expectation of the minimax value as a function of these potential future probability distributions. This is used as an objective function, according to which optimal sensing-platform tasking is computed. Both open-loop and observation-feedback sensing-platform tasking controllers are developed. A simple example is studied.
Starting Page	5041
Ending Page	5046
File Size	999322
Page Count	6
File Format	PDF
ISBN	9781424420780
ISSN	07431619
DOI	10.1109/ACC.2008.4587293
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Probability distribution Unmanned aerial vehicles Costs Minimax techniques Random variables Humans Stochastic processes Open loop systems Automatic control Vehicle dynamics
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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