Hidden Markov Model-Based Sense-Through-Foliage Target Detection Approach

  • Ganlin ZhaoEmail author
  • Qilian Liang
  • Tariq S. Durrani
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


In this paper, we propose sense-through-foliage targetdetection approach based on Hidden Markov Models (HMMs). Separate Hidden Markov Models are trained for signals containing target signature and no target (clutter), respectively. Less correlated features are selected as input of Hidden Markov Models for training and testing. Foliage data is collected from three different UWB radar locations, and experimental results show that position 1 data gives the best detection result. All three locations have above 0.8 AUC from the ROC curves.


Radar target detection Foliage UWB HMM 



This work was supported in part by NSFC under Grant 61731006, 61771342, 61711530132, Royal Society of Edinburgh, and Tianjin Higher Education Creative Team Funds Program.


  1. 1.
    Rabiner LR. A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE. 1989;77(2):257–86.CrossRefGoogle Scholar
  2. 2.
    Lee KF, Hon HW. Speaker-independent phone recognition using hidden Markov models. IEEE Trans Acoust Speech Signal Process. 1989;37(11):1641–8.CrossRefGoogle Scholar
  3. 3.
    Hahn S, et al. Comparing stochastic approaches to spoken language understanding in multiple languages. IEEE Trans Audio Speech Lang Process. 2011;19(6):1569–83.CrossRefGoogle Scholar
  4. 4.
    Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278–324.CrossRefGoogle Scholar
  5. 5.
    Sauper C, Haghighi A, Barzilay R. Incorporating content structure into text analysis applications. In: Proceedings of the 2010 conference on empirical methods in natural language processing (EMNLP ’10). Stroudsburg, PA, USA: Association for Computational Linguistics; 2010. p. 377–87.Google Scholar
  6. 6.
    Gauvain JL, Lee C-H. Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains. IEEE Trans Speech Audio Process. 1994;2(2):291–8.CrossRefGoogle Scholar
  7. 7.
    Dill C. Foliage Penetration (Phase II) Field Test: Narrowband versus Wideband Foliage Penetration. Final Report of Contract Number F41624-03-D-7001/04, July 2005 to Feb 2006.Google Scholar
  8. 8.
    Liang J, Liang Q, Samn SW. Foliage clutter modeling using the UWB radar. In: 2008 IEEE international conference on communications, Beijing, 2008; p. 1937–41.Google Scholar
  9. 9.
    Liang Q, Samn SW, Cheng X. UWB radar sensor networks for sense-through-foliage target detection. In: 2008 IEEE international conference on communications, Beijing, 2008; p. 2228–32.Google Scholar
  10. 10.
    Liang Q, Cheng X, Huang S, Chen D. Opportunistic sensing in wireless sensor networks: theory and applications. IEEE Trans Comput. 2014;63(8):2002–10.MathSciNetCrossRefGoogle Scholar
  11. 11.
    Liang Q. Situation understanding based on heterogeneous sensor networks and human-inspired favor weak fuzzy logic system. IEEE Syst J. 2011;5(2):156–63.CrossRefGoogle Scholar
  12. 12.
    Liang Q, Cheng X, Samn SW. NEW: Network-enabled electronic warfare for target recognition. IEEE Trans Aerosp Electron Syst. 2010;46(2):558–68.CrossRefGoogle Scholar
  13. 13.
    Liang Q, Cheng X. KUPS: Knowledge-based ubiquitous and persistent sensor networks for threat assessment. IEEE Trans Aerosp Electron Syst. 2008;44(3):1060.CrossRefGoogle Scholar
  14. 14.
    Peng H, Long F, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell. 2005;27(8):1226–38.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Electrical EngineeringUniversity of Texas at ArlingtonArlingtonUSA
  2. 2.Department of Electronic and Electrical EngineeringUniversity of StrathclydeGlasgowScotland, UK

Personalised recommendations