Abstract
Some traditional Wi-Fi indoor passive human detection systems only extract the coarse-grained statistical information such as the variance, which leads to low detection accuracy and poor adaptability. To solve the problem, we propose a new coherence histogram for Wi-Fi indoor passive people detection. In the histogram construction process, the method leverages time continuity relationship between received signal strength (RSS) measurements. The coherence histogram captures not only the occurrence probability of signals but also the time relationship between adjacent measurements. Compared to statistical features, the coherence histogram has more effective fine-grained information. The feature vector consists of coherence histograms is used to train the classifier. To eliminate the position drift problem, the Allen time logic helps to establish the transfer relationship between the sub-areas, we correct the results to improve the location accuracy. Compared with the classic passive human detection technology, the F1-measure is improved by nearly 5%.
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Tian, Z., Zhang, X., Li, L. (2020). Coherence Histogram Based Wi-Fi Passive Human Detection Approach. In: Gao, H., Feng, Z., Yu, J., Wu, J. (eds) Communications and Networking. ChinaCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-030-41117-6_9
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DOI: https://doi.org/10.1007/978-3-030-41117-6_9
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