Abstract
The definitions of anomaly detection and big data were presented. Due to the sampling and storage burden of anomaly detection in big data, compressive sensing theory was introduced and used in anomaly detection algorithm. The anomaly detection criterion based on wavelet packet transform and statistic process control theory was deduced. The anomaly detection method was used for through wall human detection. The experiments for detecting human behind Brick wall based on UWB radar signal was carried out. The results showed that the proposed anomaly detection algorithm could effectively detect the existence of human being through compressed signals.
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Acknowledge
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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Wang, W., Lu, D., Zhou, X., Zhang, B., Mu, J. (2014). On-Line Anomaly Detection in Big Data Based on Compressive Sensing. In: Zhang, B., Mu, J., Wang, W., Liang, Q., Pi, Y. (eds) The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 246. Springer, Cham. https://doi.org/10.1007/978-3-319-00536-2_122
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DOI: https://doi.org/10.1007/978-3-319-00536-2_122
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