AudioGuard: omnidirectional indoor intrusion detection using audio device

Tianben Wang, Zhangben Li, Honghao Yan, Xiantao Liu, Boqin Liu, Shengjie Li, Zhongyu Ma, Jin Hu, Daqing Zhang, Tao Gu

Research output: Contribution to journalArticlepeer-review


Indoor intrusion detection is a critical task for home security. Previous works in intrusion detection suffer from the problems such as blind spots in non-line-of-sight (NLOS) areas, restricted device locations, massive offline training required, and privacy concern. In this article, we design and implement an omnidirectional indoor intrusion detection system, named AudioGuard, using only a pair of speaker and microphone. Au- dioGuard is able to detect both line-of-sight (LOS) and NLOS intrusions. Our observation of acoustic signal propagation in an indoor environment shows that there exist abundant multipath reflections and human movement introduces Doppler shift in echo signals. We hence capture periodical Doppler shift caused by intruder's walking motion to detect intrusion. Specifically, we first extract the Doppler shift embedded in echo signals, and we then propose a periodicity polarization method to cancel out the impact of the change of radial angle and the distance on periodicity of Doppler shift. Finally, we detect intrusion by measuring periodicity of Doppler shift over time. Extensive experiments show that AudioGuard achieves a miss report rate of 0% and 1.75% for LOS and NLOS intrusion, respectively, and a false alarm rate of 4.17%.

Original languageEnglish
Article number4
Pages (from-to)1–22
Number of pages22
JournalACM Transactions on Internet of Things
Issue number1
Early online date16 Dec 2023
Publication statusPublished - Feb 2024


  • Indoor intrusion detection
  • acoustic sensing
  • periodic doppler shift


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