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:本文用在线主动学习算法处理目标本地化问题,该算法由每个节点的分布式、简单和快速计算方法界定,没有参数可调和,而且每个媒介的目标位置估计值与中央最大似值估测器几乎相等。 ISEE.U在每种媒介上走得很近,找到一个能最大限度地实现本地化准确性的控制器。 我们不采取特定的目标动态,因此,我们的方法在面对无法预测的目标时是稳健的。 每个媒介通过对渔业信息矩阵进行局部估计计算,计算出能够最大限度地提高总体目标位置准确性的控制值。 我们比较了拟议方法与艺术算法状态,当目标移动没有遵循规定的轨迹时,即使我们的方法在一个中央CPU运行时,我们用x100的计算时间也比它差得多。