In this paper, the unlabeled target localization problem in a wireless sensor network is addressed. Sensors of the network are deployed in a two-dimensional surveyed area and measure their distance to a target. A central unit processes the data delivered by the sensors and is tasked to infer the position of a target, but it must do so without knowing the association between the received data and the identity of the delivering sensors. This setting of unlabeled inference is attracting much attention in recent years due to its relevance to emerging applications involving big data. The present article exploits the underlying geometrical structure of the inference problem to develop two unlabeled localization approaches based on range measurements, referred to as partition and intersection points methods, respectively. The design of these methods is based on analytical results while their performance assessment is conducted by computer experiments. We find that the partition method should be preferred in the regime of low signal-to-noise ratio and networks consisting of a small number of sensors, while the intersection points method is more suitable to solve the target localization problem with large networks.

中文翻译：

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通过未标记的距离测量进行目标定位

在本文中，解决了无线传感器网络中未标记的目标定位问题。网络传感器部署在二维测量区域并测量它们与目标的距离。中央单元处理传感器传送的数据并负责推断目标的位置，但它必须在不知道接收到的数据与传送传感器的身份之间的关联的情况下这样做。由于其与涉及大数据的新兴应用程序的相关性，这种未标记推理的设置近年来备受关注。本文利用推理问题的基本几何结构来开发两种基于距离测量的未标记定位方法，分别称为分区和交点方法。这些方法的设计基于分析结果，而它们的性能评估是通过计算机实验进行的。我们发现在低信噪比和由少量传感器组成的网络的情况下应该首选划分方法，而交点方法更适合解决大型网络的目标定位问题。