This paper addresses target localization with an online active learning
algorithm defined by distributed, simple and fast computations at each node,
with no parameters to tune and where the estimate of the target position at
each agent is asymptotically equal in expectation to the centralized
maximum-likelihood estimator. ISEE.U takes noisy distances at each agent and
finds a control that maximizes localization accuracy. We do not assume specific
target dynamics and, thus, our method is robust when facing unpredictable
targets. Each agent computes the control that maximizes overall target position
accuracy via a local estimate of the Fisher Information Matrix. We compared the
proposed method with a state of the art algorithm outperforming it when the
target movements do not follow a prescribed trajectory, with x100 less
computation time, even when our method is running in one central CPU