A New Method of Target Identification in UWB Communication System Based on Smooth Pseudo Wigner Ville Distribution and Semi-supervised Clustering

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 246)

Abstract

Ultra Wideband (UWB) technology has been widely used for target identification with its strong penetrability, high resolution and good anti-interference ability. This paper proposes a new method to detect and classify target surrounded by foliage based on the real data collected from the UWB communication system. Different targets between the transmitter and receiver affect the signal differently, so the received signal contains lots of information about the target. We use smooth pseudo Wigner Ville (SPWVD) distribution to extract the feature vector of the signal and apply the semi-supervised method to realize the target identification. The experimental result shows that this method is very effective. It provides a potential way of target identification in normal UWB communication system.

Keywords

UWB technology Target identification Smooth pseudo Wigner Ville distribution Semi-supervised clustering 

Notes

Acknowledgement

This work was supported by NSFC (61171176).

References

  1. 1.
    Cao Qiu Sheng, Liu He Jun (2012) UAV systems analysis for jungle target identification based on UWB radar. Journal of China Academy of Electronics and Information Technology 8(4)Google Scholar
  2. 2.
    Editorial Committee of Journal on communication,The UWB technology Journal on communication, Journal on communication,2005.10(26)Google Scholar
  3. 3.
    Huang Qiu,Wu ShiYou (2011) The imaging algorithm of moving body target tracking based on UWB radar. Chinese Journal of Electronics 3(3)Google Scholar
  4. 4.
    S.Venkatesh,R.M.Buehrer (2007) “Non-line-of-sight identification in ultra-wide band System based on received signal statistics”, Microwaves, Antennas & Propagation, IET, vol 1Google Scholar
  5. 5.
    Minglei You (2012) A method of obstacle identification based on UWB and selected bispectra, lecture notes in electrical engineering, communications, signal processing, and systems: the 2012 Proceedings of the international conference on communications, signal processing, and systems, p 373–382Google Scholar
  6. 6.
    Junqin He (2012) A method of target detection and identification based on RPROP and UWB channel characteristic parameters, 2012 I.E. Globecom Workshops, GC Workshop 2012, p 1460–1463Google Scholar
  7. 7.
    Chang C-C (2012) Semi-supervised clustering with discriminative random fields. Pattern Recognition, 45(12)Google Scholar
  8. 8.
    Xiao Y, Yu J (2008) Semi-supervised clustering based on affinity propagation algorithm. Journal of Software 9(11):2803–2813Google Scholar
  9. 9.
    Zhou HongXing, Zhou XiaoBo, LI Yan Da (2009) Time frequency analysis: backtracking and Prospects. Chinese Journal of Electronics 9(9)Google Scholar
  10. 10.
    David P. Casasent, Tien-Hsin Chao (2007) Object detection in hyperspectral imagery by using K-means clustering algorithm with preprocessing Alam M.S. Optical Pattern Recognition XVIII, Orlando, Florida, USAGoogle Scholar
  11. 11.
    Jing Guo, Xiao Ping Zeng (2010) A time-frequency algorithm for noisy BSS model, 2010 international conference on signal and information processing 1Google Scholar
  12. 12.
    Yue Ye Qing, Xu Zheng (2008) The appliance of smooth pseudo Wigner Ville distribution in detecting Harmonics and voltage change in power system, Relay 8Google Scholar
  13. 13.
    Yang Shu Ying (2011) Pattern recognition and intelligent computing: realization with matlab, Publishing House of Electronics Industry, p 247, 8Google Scholar
  14. 14.
    Atapattu, Saman (2011) Spectrum sensing via energy detector in low SNR, IEEE International conference on communications, 2011 I.E. international conference on communications, ICC 2011Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Key Lab of Universal Wireless CommunicationBeijing University of Posts & TelecommunicationsBeijingChina

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