Abstract
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.
Highlights
The field of human detection has many important applications, ranging from autonomous vehicle safety [1], smart building surveillance [2], and site security [3], to critical disaster relief operations
principal component analysis (PCA) and recursive feature elimination with logistic regression (RFE-Logistic regression (LR))-based algorithms are applied to dynamically select the frequency bands that are sensitive to Human occupancy detection (HOD) and reconfigure the cognitive radio (CR)
In order to quantify the overall accuracy of the occupancy detection result, the actual performance was evaluated by a confusion matrix with the equations and the calculation process as follows
Summary
The field of human detection has many important applications, ranging from autonomous vehicle safety [1], smart building surveillance [2], and site security [3], to critical disaster relief operations. The existing human occupancy sensing modalities include a visual camera [4], as well as lidar [5], radar [6,7], infrared [8], and ultrasonic sensors [9]. Compared to active modalities, implementing countermeasures against a passive modality becomes difficult, as rather than relying on a transmitter whose activity might be detected with equipment, passive modalities instead exploit information that can be collected without an active signal source Several such examples of passive sensing-based technologies include photographic, thermal, electric field, chemical, infrared, and seismic signatures. Online training is applied on this base model by retraining it with the newly collected and dynamically selected RF band data at a regular basis, depending on the fluctuation level and changing frequency of the wireless signals. Results demonstrate traditional classifiers achieve a better performance for human detection, using much less training samples and number of frequency bands than the CNN.
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