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Target Tracking and Classification from Labeled and Unlabeled Data in Wireless Sensor Networks

机译：无线传感器网络中带标签和无标签数据的目标跟踪和分类

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摘要

Tracking the locations and identities of moving targets in the surveillance area of wireless sensor networks is studied. In order to not rely on high-cost sensors that have been used in previous researches, we propose the integrated localization and classification based on semi-supervised learning, which uses both labeled and unlabeled data obtained from low-cost distributed sensor network. In our setting, labeled data are obtained by seismic and PIR sensors that contain information about the types of the targets. Unlabeled data are generated from the RF signal strength by applying Gaussian process, which represents the probability of predicted target locations. Finally, by using classified unlabeled data produced by semi-supervised learning, identities and locations of multiple targets are estimated. In addition, we consider a case when the labeled data are absent, which can happen due to fault or lack of the deployed sensor nodes and communication failure. We overcome this situation by defining artificial labeled data utilizing characteristics of support vector machine, which provides information on the importance of each training data point. Experimental results demonstrate the accuracy of the proposed tracking algorithm and its robustness to the absence of the labeled data thanks to the artificial labeled data.
机译：研究了在无线传感器网络的监视区域中跟踪移动目标的位置和身份。为了不依赖先前研究中使用的高成本传感器，我们提出了一种基于半监督学习的集成定位和分类方法，该方法使用从低成本分布式传感器网络获得的标记和未标记数据。在我们的设置中，标记的数据是通过地震和PIR传感器获得的，其中包含有关目标类型的信息。通过应用高斯过程从RF信号强度生成未标记的数据，该过程代表了预测目标位置的概率。最后，通过使用半监督学习产生的分类未标记数据，可以估计多个目标的身份和位置。另外，我们考虑缺少标记数据的情况，这可能是由于故障或缺少部署的传感器节点以及通信失败而发生的。我们通过利用支持向量机的特征定义人工标记的数据来克服这种情况，该特征提供了有关每个训练数据点重要性的信息。实验结果证明了所提出的跟踪算法的准确性及其由于缺少人工标记数据而缺乏标记数据的鲁棒性。

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