Wiring Effects Mitigation for Through-Wall Human Motion Micro-Doppler Signatures Using a Generative Adversarial Network

Shuoguang Wang, Qiang An, Shiyong Li*, Guoqiang Zhao, Houjun Sun

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Through-wall detection and recognition of human motions via radar is of great benefit to public security and emergency service applications. The micro-Doppler signatures extracted from the targets of interest in motion typically contain distinct inner-individual motion features, which is the key to human identification and motion classification. However, no research so far considered a very common application scenario, where the conductive wires buried in the wall are in a powering on mode, let alone study its potential effect on the collected signatures of motion behind wall. As it should be anticipated, strong interference components would be brought in the obtained micro-Doppler signatures, and the subsequent motion recognition would be severely affected. In this paper, we, for the first time, report the effect of the buried live wire on the micro-Doppler signatures. Specifically, a micro-Doppler signature enhancement method, named range-max time-frequency representation (R-max TFR) is utilized to obtain feature enhanced micro-Doppler signatures of behind wall human motions. And to mitigate the clutter components introduced by the buried live wire, the effect is first modeled as an impulse response with its center located at a fixed frequency instance in the R-max TFR map. Then, a novel technique based on conditional Generative Adversarial Network (cGAN), is proposed to fulfill the goal. Both numerical and experimental results, as well as comparisons with other classical de-clutter methods, demonstrate the effectiveness and superiority of the proposed de-wiring cGAN framework in suppressing the wiring effect in behind wall micro-Doppler signatures.

Original languageEnglish
Article number9349526
Pages (from-to)10007-10016
Number of pages10
JournalIEEE Sensors Journal
Volume21
Issue number8
DOIs
Publication statusPublished - 15 Apr 2021

Keywords

  • Through-wall human motion detection
  • conditional Generative Adversarial Network (cGAN)
  • de-wiring technique
  • range-max time-frequency representation
  • the wiring effect

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