Abstract
UWB radar with high-range resolution and strong penetration ability can be used to separate multiple human targets in a complex environment. The through-wall human being detection with UWB radar has been relatively mature in the current study. This paper extracts the characteristic parameters which are related to the human targets from the received signals as the sample data. And used machine learning based on the KNN (K nearest neighbor) classification algorithm to identify and classify the through-wall human being status. Experimental results showed that the KNN classification algorithm effectively distinguished three statuses of through-wall human being and reached the prospective goal.
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