Impact of Seasonal Variations on Foliage Penetration Experiment: A WSN-Based Device-Free Sensing Approach

Yi Zhong, Yang Yang, Xi Zhu, Yan Huang*, Eryk Dutkiewicz, Zheng Zhou, Ting Jiang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

Abstract

Foliage penetration (FOPEN) has been found to be a critical mission for a variety of applications, ranging from surveillance to military. Recently, an emerging technology, namely wireless sensor network (WSN)-based device-free sensing (DFS), has been introduced to the domain of FOPEN. This technology only utilizes radio-frequency signals for target detection and classification; thus, no additional hardware is required, just a wireless transceiver. Although the feasibility of using this technology for human detection indoors has been explored to some extent, it is questionable if the same technology can be transferred to outdoors. As far as FOPEN is concerned, the impact of seasonal variations on detection accuracy can be severe. To address this concern, in this paper, an experiment is conducted in four seasons, and how to ensure reasonable detection accuracy with seasonal variations is intensively investigated. To fully evaluate the potential of using the WSN-based DFS for FOPEN, an impulse-radio ultrawideband technology-based prototype is used to collect data samples in different seasons. Unlike the conventional approach based on a combination of statistical properties of received-signal strength and a support vector machine, this approach adopts two special measures for performance enhancement. One measure is to use a higher order cumulant (HOC) algorithm for feature extraction, so that the impact on detection accuracy due to unwanted clutters can be minimized. The other one is to determine the optimal parameters of the classifier by means of a flower pollination algorithm. Consequently, the adverse effects on detection accuracy due to variations of weather conditions in four seasons can be accommodated. According to the experimental result, it is shown that the average classification accuracy of the presented approach can be improved by at least 20% under all seasons with an ensured robustness.

Original languageEnglish
Article number8306281
Pages (from-to)5035-5045
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume56
Issue number9
DOIs
Publication statusPublished - Sept 2018
Externally publishedYes

Keywords

  • Device-free sensing (DFS) technology
  • feature extraction
  • flower pollination algorithm (FPA)
  • foliage penetration (FOPEN)
  • higher order cumulant (HOC)
  • impulse-radio ultrawideband (IR-UWB)
  • support vector machine (SVM)

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Zhong, Y., Yang, Y., Zhu, X., Huang, Y., Dutkiewicz, E., Zhou, Z., & Jiang, T. (2018). Impact of Seasonal Variations on Foliage Penetration Experiment: A WSN-Based Device-Free Sensing Approach. IEEE Transactions on Geoscience and Remote Sensing, 56(9), 5035-5045. Article 8306281. https://doi.org/10.1109/TGRS.2018.2804346