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Asset assignment and scheduling algorithms were developed and implemented to support a team-in-the-loop planning experiment conducted at the Naval Postgraduate School (NPS) in March 2009. The experiment examined planning and information flows among three cells in an abstracted and simplified Maritime Operations Center (MOC). This paper describes two optimization-based modules that focused on the Future...
In this paper, a hidden Markov model (HMM)-based dynamic sensor scheduling problem is formulated, and solved using rollout concepts to overcome the computational intractability of the dynamic programming (DP) recursion. The problem considered here involves dynamically sequencing a set of sensors to minimize the sum of sensor cost and the HMM state estimation error cost. The surveillance task is modeled...
We consider the problem of optimal allocation of measurement resources, when: (1) the total measurement budget and time duration of measurements are fixed, and (2) the cost of an individual measurement varies inversely with the (controllable) measurement accuracy. The objective is to determine the time-distribution of measurement variances that minimizes a measure of error in estimating a discrete-time,...
In this paper, we formulate and solve a sequential decision problem of a special type arising in M-ary hypothesis testing. In this problem, a team, comprised of a primary decisionmaker (DM) and N geographically separated subordinate DMs, is faced with the task of deciding which one of the M prespecified hypotheses is true, subject to the constraint that the team is allowed to make at most K measurement...
This paper considers a distributed binary hypothesis testing problem in which a number of subordinate decision makers (DMs, sensors) transmit thir opinions, based on their own data, to a primary decision maker who combines these opinions and makes the final team decision. The primary decision maker does not receive any measurments (of his own) from the environment and only acts as a fusion center...
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