Classification of animals and people based on radio-sensor network

Y. Zhong*, Zheng Zhou, Ting Jiang, Michael Heimlich, Eryk Dutkiewicz, Gengfa Fang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

1 Citation (Scopus)

Abstract

Personnel detection embedded in foliage is extremely important to border patrol, perimeter protection and search-and-rescue operations. In this paper, we explore the utility of radio-sensor network (RSN) to distinguish between humans and animals. We explore the phenomenon that signals are always affected by the presence of obstacles and identify human based on the received signals by transceivers, which leads to a potential low-cost way for personnel detection without specific sensors. In our study, the impulse radio ultra-wideband (IR-UWB) technology is selected for the RF transceiver due to the fact that it is not only energy efficient, but also robust against interferences. The principle component analysis (PCA) is applied to extract the feature vector, and a support vector machine is used as the target classifier. Experiment result with an average accuracy of 97.5% based on actual data collected in a cornfield indicates that this approach has a good capability to distinguish between human and animals in a foliage environment.

Original languageEnglish
Title of host publication2016 16th International Symposium on Communications and Information Technologies, ISCIT 2016
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages113-116
Number of pages4
ISBN (Electronic)9781509040995
DOIs
Publication statusPublished - 21 Nov 2016
Event16th International Symposium on Communications and Information Technologies, ISCIT 2016 - Qingdao, China
Duration: 26 Sep 201628 Sep 2016

Other

Other16th International Symposium on Communications and Information Technologies, ISCIT 2016
CountryChina
CityQingdao
Period26/09/1628/09/16

Keywords

  • IR-UWB
  • network
  • personnel detection
  • radio-sensor
  • support vector machine

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