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

页码：63 / 65

页数：3

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