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Machine Learning-Based Human Recognition Scheme Using a Doppler Radar Sensor for In-Vehicle Applications

Title
Machine Learning-Based Human Recognition Scheme Using a Doppler Radar Sensor for In-Vehicle Applications
Authors
Hyun, EuginJin, Young-SeokPark, Jae-HyunYang, Jong-Ryul
DGIST Authors
Hyun, Eugin; Jin, Young-Seok; Park, Jae-Hyun; Yang, Jong-Ryul
Issue Date
2020-11
Citation
Sensors, 20(21), 1-20
Type
Article
Article Type
Article
Author Keywords
passenger detectionCW radarradar feature vectorradar machine learning
ISSN
1424-8220
Abstract
In this paper, we propose a Doppler spectrum-based passenger detection scheme for a CW (Continuous Wave) radar sensor in vehicle applications. First, we design two new features, referred to as an ‘extended degree of scattering points’ and a ‘different degree of scattering points’ to represent the characteristics of the non-rigid motion of a moving human in a vehicle. We also design one newly defined feature referred to as the ‘presence of vital signs’, which is related to extracting the Doppler frequency of chest movements due to breathing. Additionally, we use a BDT (Binary Decision Tree) for machine learning during the training and test steps with these three extracted features. We used a 2.45 GHz CW radar front-end module with a single receive antenna and a real-time data acquisition module. Moreover, we built a test-bed with a structure similar to that of an actual vehicle interior. With the test-bed, we measured radar signals in various scenarios. We then repeatedly assessed the classification accuracy and classification error rate using the proposed algorithm with the BDT. We found an average classification accuracy rate of 98.6% for a human with or without motion. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
URI
http://hdl.handle.net/20.500.11750/12756
DOI
10.3390/s20216202
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Related Researcher
  • Author Hyun, Eugin  
  • Research Interests Radar system, Radar signal processing, Automotive radar, Surveillance radar, Commercial radar, Defence radar,
Files:
Collection:
Division of Automotive Technology1. Journal Articles


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