On the Use of Low-Cost Radars and Machine Learning for In-Vehicle Passenger Monitoring

被引：0
作者：
Abedi, Hajar [1 ]
Luo, Shenghang [1 ]
Shaker, George [1 ]
机构：
[1] Univ Waterloo, Waterloo, ON, Canada
来源：
2020 IEEE 20TH TOPICAL MEETING ON SILICON MONOLITHIC INTEGRATED CIRCUITS IN RF SYSTEMS (SIRF) | 2020年
关键词：
FMCWR RADAR; CAPON BEAM-FORMER; MACHINE LEARNING;
D O I：
10.1109/sirf46766.2020.9040191
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we use a low-cost low-power mm-wave frequency modulated continuous wave (FMCW) radar for in-vehicle occupant monitoring. We propose an algorithm to identify occupied seats. Instead of using a high-resolution radar which increases the cost and area, we integrate machine learning algorithms with the results of covariance-based angle of arrival estimation Capon beamformer. We apply three classifiers, support vector machine (SVM), K-Nearest Neighbors (KNN) and Random Forest (RF). Our proposed system using SVM classifier achieved 96% accuracy on average in identifying the occupied seats in the test vehicles.
引用
收藏
页码：63 / 65
页数：3
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