On-Line Anomaly Detection in Big Data Based on Compressive Sensing

  • Wei Wang
  • Dunqiang Lu
  • Xin Zhou
  • Baoju Zhang
  • Jiasong Mu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 246)


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.


Anomaly detection Big data Through wall human detection Compressive sensing 



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).


  1. 1.
    Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41(3):1–72CrossRefGoogle Scholar
  2. 2.
    Chun Tung Chou, Rajib Rana, Wen Hu (2009) Energy efficient information collection in wireless sensor networks using adaptive compressive sensing. 2009 I.E. 34th conference on local computer networks, vol 10, Zurich, Switzerland, p 443–450Google Scholar
  3. 3.
    Jin Wang, Shaojie Tang, Baocai Yin, (2012) Data gathering in wireless sensor networks through intelligent compressive sensing. 2012 Proceedings IEEE INFOCOM, p 603–611Google Scholar
  4. 4.
    Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306CrossRefMathSciNetGoogle Scholar
  5. 5.
    Candès E (2006) Compressive sampling. In: Proceedings of international congress of mathematicians. European Mathematical Society Publishing House, Madrid, Spain, p 1433–1452Google Scholar
  6. 6.
    Sun Z, Chang CC (2004) Statistical wavelet-based method for structural health monitoring. J Struct Eng 130(7):1055–1062CrossRefGoogle Scholar
  7. 7.
    Candès E, Wakin M (Mar. 2008) An introduction to compressive sampling. IEEE Signal Process Mag 25:21–30CrossRefGoogle Scholar
  8. 8.
    Lei Xu, Qilian Liang, Xiuzhen Cheng, Dechang Chen (2013) Compressive sensing in distributed radar sensor networks using pulse compression waveforms. EURASIP J Wirel Commun and Netw. doi:10.1186/1687-1499-2013-36Google Scholar
  9. 9.
    Lei X, Liang Q (2012) Zero correlation zone sequence pair sets for MIMO radar. IEEE Trans Aerosp Electron Syst 48(3):2100–2113CrossRefGoogle Scholar
  10. 10.
    Lei Xu, Qilian Liang (2010) Orthogonal pulse compression codes for mimo radar system. IEEE Globecom, Miami, FLGoogle Scholar
  11. 11.
    Lei Xu, Qilian Liang (2010) Waveform design and optimization in radar sensor network. IEEE Globecom, Miami, FLGoogle Scholar
  12. 12.
    Liang Q, Mendel JM (2000) Design interval type-2 fuzzy logic systems using SVD-QR method: rule reduction¿. Int J Intel Syst 15(10):939–957CrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Wei Wang
    • 1
  • Dunqiang Lu
    • 1
  • Xin Zhou
    • 1
  • Baoju Zhang
    • 1
  • Jiasong Mu
    • 1
  1. 1.College of Electronic and Communication EngineeringTianjin Normal UniversityTianjinChina

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