Modeling and Analysis of Exhaustive Probabilistic Search
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Authors
Chung, Timothy H.
Silvestrini, Rachel T.
Subjects
probabilistic search theory
autonomous systems
expected time to decision
design of experiments
generalized linear models
autonomous systems
expected time to decision
design of experiments
generalized linear models
Advisors
Date of Issue
2014
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Abstract
This article explores a probabilistic formulation for exhaustive search of a bounded area by a single searcher for a
single static target. The searcher maintains an aggregate belief of the target’s presence or absence in the search area, concluding
with a positive or negative search decision on crossing of decision thresholds. The measure of search performance is defined as
the expected time until a search decision is made as well as the probability of the search decision being correct. The searcher
gathers observations using an imperfect detector, that is, one with false positive and negative errors, and integrates them in an
iterative Bayesian manner. Analytic expressions for the Bayesian update recursion of the aggregate belief are given, with theoretical
results describing the role of positive and negative detections, as well as sensitivity results for the effect of the detection errors on
the aggregate belief evolution. Statistical studies via design of simulation experiments provide insights into the significant search
parameters, including imperfect sensor characteristics, initial belief value, search decision threshold values, and the available prior
probability information. Regression analysis yields statistical models to provide prescriptive guidance on the search performance
as a function of these search parameters.
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Article
Description
The article of record as published may be located at http://dx.doi.org/10.1002/nav.21574
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Operations Research
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.