Internet of Mission-Critical Things: Human and Animal Classification—A Device-Free Sensing Approach

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Journal Article
IEEE Internet of Things Journal, 2017
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IEEE The well-known Internet-of-things is recently being considered for critical missions, such as search and rescue, surveillance and border patrol. One of the most critical issues that these applications are currently facing is how to correctly distinguish between human and animal targets in a cost-effective way. In this work, we present a relatively low-cost but robust approach that uses a combination of device-free sensing and machine-learning technologies to tackle this issue. In order to validate the feasibility of the presented approach, a variety of data is collected in a cornfield using impulse-radio ultra-wideband transceivers. These data are then used to investigate the influence of different statistical properties of the RF signal on the accuracy of human/animal target classification. Based on the probability density function of different statistical properties, two distinguishing features for target classification are found, namely standard deviation and root mean spread delay spread. Using them, the impact on the classification accuracy due to different classifiers, number of training samples and different values of signal-to-noise ratio is extensively verified. Even with the worst case, the classification accuracy of the system is still better than 91targets (including goats and dogs), which indicates that the presented approach has a great potential to be deployed in the near future.
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