Tracking the Occluded Indoor Target with Scattered Millimeter Wave Signal

Yinda Xu, Xinjue Wang, Juhani Kupiainen, Joonas Sae, Jani Boutellier, Jari Nurmi, Bo Tan

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
11 Downloads (Pure)

Abstract

The popularity of mobile robots in factories, warehouses, and hospitals has raised safety concerns about human-machine collisions, particularly in nonline-of-sight (NLoS) scenarios such as corners. Developing a robot capable of locating and tracking humans behind the corners will greatly mitigate risk. However, most of them cannot work in complex environments or require a costly infrastructure. This article introduces a solution that uses the reflected and diffracted millimeter wave (mmWave) radio signals to detect and locate targets behind the corner. Central to this solution is a localization convolutional neural network (L-CNN), which takes the angle-delay heatmap of the mmWave sensor as input and infers the potential target position. Furthermore, a Kalman filter is applied after L-CNN to improve the accuracy and robustness of estimated locations. A red-green-blue-depth (RGB-D) camera is attached to the mmWave sensor as the annotation system to provide accurate position labels. The results of the experimental evaluation demonstrate that our data-driven approach can achieve remarkable positioning accuracy at the 10-cm level without extensive infrastructure. In particular, the approach effectively mitigates the adverse effects of diffraction and multibounce phenomena, making the system more resilient.

Original languageEnglish
Pages (from-to)38102-38112
Number of pages11
JournalIEEE Sensors Journal
Volume24
Issue number22
Early online date23 Aug 2024
DOIs
Publication statusPublished - 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • Cameras
  • Indoor positioning
  • Millimeter wave communication
  • Optical imaging
  • Optical sensors
  • Radar tracking
  • Robot sensing systems
  • Robots
  • angle-delay estimation
  • convolutional neural network
  • cross-modal training
  • frequency-modulated continuous-wave radar
  • nonline-of-sight tracking
  • robotics
  • Angle-delay estimation
  • nonline-of-sight (NLoS) tracking
  • convolutional neural network (CNN)
  • indoor positioning
  • frequency-modulated continuous-wave (FMCW) radar

Fingerprint

Dive into the research topics of 'Tracking the Occluded Indoor Target with Scattered Millimeter Wave Signal'. Together they form a unique fingerprint.

Cite this