  In this paper we simulate an ensemble of cooperating, mobile sensing agents
that implement the cyclic stochastic optimization (CSO) algorithm in an attempt
to survey and track multiple targets. In the CSO algorithm proposed, each agent
uses its sensed measurements, its shared information, and its predictions of
others' future motion to decide on its next action. This decision is selected
to minimize a loss function that decreases as the uncertainty in the targets'
state estimates decreases. Only noisy measurements of this loss function are
available to each agent, and in this study, each agent attempts to minimize
this function by calculating its stochastic gradient. This paper examines, via
simulation-based experiments, the implications and applicability of CSO
convergence in three dimensions.
