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.

翻译：本文针对目标定位问题提出了一种在线主动学习算法，该算法在各节点处实现分布式、简单且快速的计算，无需调整参数，且每个智能体对目标位置的估计在期望上渐近等同于集中式最大似然估计。ISEE.U算法利用各节点获取的含噪距离测量值，寻找能最大化定位精度的控制策略。我们不假设特定的目标动力学模型，因此该方法在面对不可预测目标时具有鲁棒性。每个智能体通过局部估计的Fisher信息矩阵计算能最大化整体目标定位精度的控制量。我们将所提方法与一种先进算法进行了比较，当目标运动不遵循预设轨迹时，即使该方法在单个中央CPU上运行，其计算时间也降低了两个数量级，且定位性能更优。