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A Scheme for Group Target Tracking in WSN Based on Boundary Detecting

机译:基于边界检测的无线传感器网络中的组目标跟踪方案

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摘要

When a mass of individual targets move closely, it is unpractical or unnecessary to localize and track every specific target in wireless sensor networks (WSN) under some situation, but they can be tracked as a whole by view of group target. A scheme for tracking group target in WSN based on binary sensing model is proposed in this paper. In order to obtain the global estimated position information of group target and decrease the amount of energy spent on active sensing and communications, it only needs to estimate and track the boundary of group target, and let be involved as few as possible sensors into the tracking process. So, the scheme consists of three parts mainly. First, a flexible Boundary Detecting Model is proposed to divide sensors into three categories, named INNER, BOUNDARY and OUTER separately, where, only BOUNDARY sensors take part in tracking group target, and the number can be adjustable too. Second, BOUNDARY sensors are divided into several clusters according to a new clustering algorithm based on Boundary Clustering Model, which just divides BOUNDARY sensors into several clusters, without considering the INNER and OUTER ones. Third, as a first attempt, a divide-merge algorithm using convex hull is designed to localize and track group target. In this algorithm, group target's boundary is divided into several small pieces, and each one is enclosed by a convex hull which is constructed through a cluster of BOUNDARY sensors. Then, these convex hulls are sent back to a sink respectively, and merged into a big convex hull as the group target's contour. According to a criteria of precision evaluation introduced in this paper, the simulation experiments confirm efficiency and accuracy of the method.
机译:当大量的单个目标紧密移动时,在某些情况下定位和跟踪无线传感器网络(WSN)中的每个特定目标都是不切实际或不必要的,但是可以通过组目标的整体进行跟踪。提出了一种基于二元感知模型的无线传感器网络中的目标跟踪方案。为了获得组目标的全局估计位置信息并减少在主动感测和通信上花费的能量,只需要估计并跟踪组目标的边界,并让尽可能少的传感器参与跟踪即可处理。因此,该方案主要包括三个部分。首先,提出了一种灵活的边界检测模型,将传感器分为三类,分别命名为INNER,BOUNDARY和OUTER,其中只有BOUNDARY传感器参与跟踪组目标,并且数目也可以调整。其次,根据基于边界聚类模型的新聚类算法,将边界传感器分为几个簇,该算法仅将边界传感器分为几个簇,而不考虑内部和外部传感器。第三,作为第一个尝试,设计了使用凸包的划分合并算法来定位和跟踪组目标。在该算法中,将组目标的边界分成几个小块,每个块都由凸包包围,该凸包通过一组BOUNDARY传感器构造而成。然后,将这些凸包分别发送回接收器,并合并为一个大凸包,作为组目标的轮廓。根据本文介绍的精度评估标准,仿真实验证实了该方法的有效性和准确性。

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