Novel Robust On-Line Indoor Occupancy Counting System Using mmWave Radar
IEEE SENSORS JOURNAL(2023)
Abstract
In this work, a novel robust on-line indoor occupancy counting approach is proposed using the millimeter- wave (mmWave) frequency-modulated continuous-wave (FMCW) radar. The acquired radar data can be represented as sparse 3-D radar point clouds. The conventional indoor occupancy counting schemes suffer from various types of errors and uncertainties emerging from mmWave radar sensors. Thus, we propose a novel feature extraction strategy to obtain the robust features for training the machine-learning-driven occupancy counter. We formulate the underlying indoor occupancy counting problem as the multiclassification problem such that the k nearest-neighbor (KNN) classifier is adopted to identify the number of occupants on line. Experimental results from real-world data demonstrate that our proposed new approach leads to a promising classification accuracy of 95.8% for indoor occupancy counting. In the comparative study based on real-world data, our proposed novel indoor occupancy counting method greatly outperforms other existing schemes.
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Key words
Radar,Millimeter wave communication,Sensors,Feature extraction,Three-dimensional displays,Training,Data acquisition,Indoor occupancy counting,k nearest-neighbor (KNN) classifier,machine learning,millimeter-wave (mmWave) frequency-modulated continuous-wave (FMCW) radar
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