Abstract
Different from the traditional static data anomaly detection, the subjects of big data stream anomaly detection has been attracting extensive attention. Through wall human being detection with UWB radar has become popular recently due to its many merits. And it is a typical big data stream mining problem when detected human being in real time. In this paper, we proposed a statistical algorithm based on spectral method for big data stream anomaly detection. The through brick wall human detection experiment was designed and the results showed that the proposed method could detect the human being with high confidence level.
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Acknowledgement
The authors would love to thank Professor Qilian Liang in University of Texas at Arlington for providing the UWB radar data. This research was supported by the Tianjin Younger Natural Science Foundation (12JCQNJC00400) and National Natural Science Foundation of China (61271411).
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Yun, Y., Wang, W. (2015). Big Data Stream Anomaly Detection with Spectral Method for UWB Radar Data. In: Mu, J., Liang, Q., Wang, W., Zhang, B., Pi, Y. (eds) The Proceedings of the Third International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-319-08991-1_26
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DOI: https://doi.org/10.1007/978-3-319-08991-1_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08990-4
Online ISBN: 978-3-319-08991-1
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