Human Occupancy Detection via Passive Cognitive Radio
1
Department of Electrical and Computer Engineering, Oakland University, Rochester, MI 48309, USA
2
Air Force Research Lab, Wright Patterson Air Force Base, Dayton, OH 45433, USA
3
Department of Mathematics, Ohio University, Athens, OH 45701, USA
4
Air Force Research Lab, Rome, NY 13441, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4248; https://doi.org/10.3390/s20154248
Received: 14 June 2020
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Revised: 15 July 2020
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Accepted: 27 July 2020
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Published: 30 July 2020
(This article belongs to the Special Issue Women in Sensors)
Human occupancy detection (HOD) in an enclosed space, such as indoors or inside of a vehicle, via passive cognitive radio (CR) is a new and challenging research area. Part of the difficulty arises from the fact that a human subject cannot easily be detected due to spectrum variation. In this paper, we present an advanced HOD system that dynamically reconfigures a CR to collect passive radio frequency (RF) signals at different places of interest. Principal component analysis (PCA) and recursive feature elimination with logistic regression (RFE-LR) algorithms are applied to find the frequency bands sensitive to human occupancy when the baseline spectrum changes with locations. With the dynamically collected passive RF signals, four machine learning (ML) classifiers are applied to detect human occupancy, including support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT), and linear SVM with stochastic gradient descent (SGD) training. The experimental results show that the proposed system can accurately detect human subjects—not only in residential rooms—but also in commercial vehicles, demonstrating that passive CR is a viable technique for HOD. More specifically, the RFE-LR with SGD achieves the best results with a limited number of frequency bands. The proposed adaptive spectrum sensing method has not only enabled robust detection performance in various environments, but also improved the efficiency of the CR system in terms of speed and power consumption.
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Keywords:
human occupancy detection; passive cognitive radio; feature selection; adaptive spectrum sensing; reconfigurable software defined radio
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MDPI and ACS Style
Liu, J.; Mu, H.; Vakil, A.; Ewing, R.; Shen, X.; Blasch, E.; Li, J. Human Occupancy Detection via Passive Cognitive Radio. Sensors 2020, 20, 4248. https://doi.org/10.3390/s20154248
AMA Style
Liu J, Mu H, Vakil A, Ewing R, Shen X, Blasch E, Li J. Human Occupancy Detection via Passive Cognitive Radio. Sensors. 2020; 20(15):4248. https://doi.org/10.3390/s20154248
Chicago/Turabian StyleLiu, Jenny, Huaizheng Mu, Asad Vakil, Robert Ewing, Xiaoping Shen, Erik Blasch, and Jia Li. 2020. "Human Occupancy Detection via Passive Cognitive Radio" Sensors 20, no. 15: 4248. https://doi.org/10.3390/s20154248
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