Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

Analysis of Search Decision Making Using Probabilistic Search Strategies

Sign In

Full text access may be available.

To access full text, please use your member or institutional sign in.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Chung, T.H. ; Dept. of Syst. Eng., Naval Postgrad. Sch., Monterey, CA, USA ; Burdick, J.W.

In this paper, we propose a formulation of the spatial search problem, where a mobile searching agent seeks to locate a stationary target in a given search region or declare that the target is absent. The objective is to minimize the expected time until this search decision of target's presence (and location) or absence is made. Bayesian update expressions for the integration of observations, including false-positive and false-negative detections, are derived to facilitate both theoretical and numerical analyses of various computationally efficient (semi-)adaptive search strategies. Closed-form expressions for the search decision evolution and analytic bounds on the expected time to decision are provided under assumptions on search environment and/or sensor characteristics. Simulation studies validate the probabilistic search formulation and comparatively demonstrate the effectiveness of the proposed search strategies.

Published in:

Robotics, IEEE Transactions on  (Volume:28 ,  Issue: 1 )