论文标题
ISEE.U：具有无法预测的目标的在线主动目标本地化
ISEE.U: Distributed online active target localization with unpredictable targets
论文作者
论文摘要
本文通过在每个节点上通过分布式，简单和快速计算定义的在线主动学习算法来解决目标定位，而没有参数可以调节，并且在每种代理处的目标位置的估计值渐近地等于预期的最大最大 - 叶状性估计器。 ISEE.U在每个代理商处采取嘈杂的距离，并找到一个最大化本地化精度的控制。我们不假定特定的目标动态，因此，在面对不可预测的目标时，我们的方法是强大的。每个代理都通过Fisher信息矩阵的局部估计来计算最大化总体目标位置精度的控制。当目标运动不遵循规定的轨迹时，我们将提出的方法与最先进的算法的状态比较了，即使我们的方法在一个中央CPU中运行，x100的计算时间也会少x100。
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.
