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Target Counting Using Binary Sensors Based on Disjoint Connected Subgraphs

机译：基于不相交的连接子图使用二元传感器的目标计数

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This paper proposes an algorithm for counting the number of distinct targets using binary proximity sensors that are deployed in a monitored region for target detection. Sensors that simultaneously detect a common target should be placed in close proximity to each other. If multiple targets exist in a monitored region, sensors that detect them at a given time are partitioned into several clusters, where each cluster corresponds to a distinct target. Based on this consideration, the proposed algorithm estimates the number of distinct targets by finding the number of clusters of target-detecting sensors. To find clusters of sensors, the concept of connectivity between target-detecting sensors is introduced. Two target-detecting sensors are recognized as being connected if they do not have any common nontarget-detecting sensors within their neighborhood. Based on the connectivity between target-detecting sensors, the sensors are partitioned into disjoint connected subgraphs. The number of such subgraphs is used as an estimator of the number of targets. Simulation experiments verify that the proposed algorithm gives good estimates of the number of distinct targets especially when a high density of sensors is deployed.
机译：本文提出了一种用于计算使用在监视区域中部署的二进制接近传感器来计算不同目标的数量的算法，用于目标检测。同时检测共同目标的传感器应彼此紧密地放置。如果在受监视区域中存在多个目标，则在给定时间检测它们的传感器被划分为几个集群，其中每个群集对应于不同的目标。基于该考虑，所提出的算法通过找到目标检测传感器的簇​​数来估计不同的目标的数量。为了找到传感器的集群，介绍了目标检测传感器之间的连接的概念。如果它们在其邻域内没有任何常见的Nontarget检测传感器，则识别出两个目标检测传感器被识别为连接。基于目标检测传感器之间的连接，传感器被划分为不相交的连接子图。这些子图的数量用作目标数量的估计器。仿真实验验证所提出的算法是否提供了良好的估计，特别是当部署高密度的传感器时的不同目标的数量。

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