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Human and Small Animal Detection Using Multiple Millimeter-Wave Radars and Data Fusion: Enabling Safe Applications.

Ana Beatriz Rodrigues Costa De Mattos1, Glauber Brante2, Guilherme L Moritz2

  • 1Department of Electrical and Electronics Engineering, Federal University of Santa Catarina (UFSC), Florianopolis 88040-900, Brazil.

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|March 28, 2024
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Summary
This summary is machine-generated.

Millimeter-wave (mmWave) radars can detect people and small animals. Using four mmWave radars with data fusion and tracking significantly improves detection sensitivity to 97.10%.

Keywords:
data fusionhuman trackingmillimeter-wavemultiple radarssmall animalswireless power transfer

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Area of Science:

  • Sensor Technology
  • Robotics and Automation
  • Biomedical Engineering

Background:

  • Millimeter-wave (mmWave) radars offer high resolution and privacy preservation, unaffected by environmental conditions.
  • Simultaneous detection of people and small animals is crucial for applications like indoor wireless energy transfer.
  • Challenges include varying target sizes and volumes, necessitating advanced detection methodologies.

Purpose of the Study:

  • To explore challenges in using mmWave radars for simultaneous detection of humans and small animals.
  • To propose innovative methods for enhancing detection accuracy in indoor environments.
  • To evaluate the effectiveness of data-fusion techniques with multiple mmWave radars.

Main Methods:

  • Utilized up to four mmWave radars in two distinct indoor positioning scenarios.
  • Investigated the detection and tracking of both humans and small animals.
  • Compared the performance of three different data-fusion methods.

Main Results:

  • Single radar without tracking yielded 46.1% sensitivity.
  • Optimal fusion method with four radars and tracking achieved 97.10% sensitivity.
  • Demonstrated significant improvement in detection sensitivity and reliability.

Conclusions:

  • Multiple mmWave radars combined with data fusion and tracking substantially enhance target detection.
  • The proposed methodologies effectively overcome challenges in detecting diverse targets.
  • This approach significantly improves sensitivity and reliability for indoor monitoring applications.