Human Detection Based on Radar Sensor Network in Natural Disaster

  • Wei WangEmail author
Part of the Springer Natural Hazards book series (SPRINGERNAT)


In recent years, natural disasters, such as earthquakes, landslides and others, have caused significant damage to people’s lives and property. Victims are often trapped in collapsed buildings. Thus the development and understanding of modern techniques for disaster relief are of immense current interest and need. As a significant advancement in wireless communication, the emerging UWB Radar Technology is a key technology that UWB is applied in object identification, which is characterized by high resolution, good anti-interference ability and strong penetrability and so on, has been widely used in various fields, including natural disaster detection, through-wall radar imaging, ground penetrating radar technology, medical imaging, target ranging and personnel positioning, disaster relief and so on. In this chapter, the author will describe some algorithms for human detection based on UWB radar sensor network in natural disaster. Firstly, we study the fuzzy pattern recognition and genetic algorithm which is used to identify the multi-status human being after the brick wall. The main characteristic parameters are selected and extracted from the received signal, and each feature parameters corresponding to a sub membership function. Through the genetic algorithm to optimize the sub membership function for constructing the membership function set. According to fuzzy pattern recognition principle of maximum degree of membership function to establish target prediction function, and used MATLAB to carry on the simulation for it. Secondly, we study the stacked denoising autoencoder algorithm in deep learning to study the through wall human target recognition under imbalanced samples of single sensor and multi-sensor data respectively. The experimental results show that the stacked denoising autoencoder algorithm in deep learning adopted herein allows more effective classification and identification of through wall human targets under imbalanced sample conditions than other algorithms, and that the identification effect with multiple sensors under a certain imbalance rate is better than that with a single sensor.


UWB radar Through-wall human being detection Fuzzy pattern recognition Genetic algorithm Imbalanced samples Autoencoder Deep learning 


  1. 1.
    Kumar A, Liang Q, Li Z, Zhang B, Wu X (2012) Experimental study of through-wall human being detection using ultra-wideband (UWB) radar. Globecom workshops (GC Wkshps), 2012 IEEEGoogle Scholar
  2. 2.
    Sahu KN, Satyam M, Naidu CD, Sankar KJ (2015) UWB propagation modeling of human being behind a concrete wall for the study of cardiac condition. In: International conference on electrical, 2015, pp 1–5Google Scholar
  3. 3.
    Yarovoy AG, Ligthart LP, Matuzas J et al (2006) UWB radar for human being detection. IEEE Aerosp Electron Syst Mag 21(3):10–14CrossRefGoogle Scholar
  4. 4.
    Attiya AM, Bayram A, Safaai-Jazi A et al (2004) UWB applications for through-wall detection. In: IEEE international symposium: antennas and propagation society, 2004. IEEE, vol 3, pp 3079–3082Google Scholar
  5. 5.
    Lubecke VM, Boric-Lubecke O, Host-Madsen A et al (2007) Through-the-wall radar life detection and monitoring. In: IEEE/MTT-S international microwave symposium. IEEE, pp 769–772Google Scholar
  6. 6.
    Chernyak V (2008) Detection problem for searching survivors in rubble with UWB radars. In: European radar conference, 2008. EuRAD 2008. IEEE, pp 44–47Google Scholar
  7. 7.
    Li J, Zeng Z, Sun J, Liu F (2012) Through-wall detection of human being’s movement by UWB radar. IEEE Geosci Remote Sens Lett 9(6):1079–1083CrossRefGoogle Scholar
  8. 8.
    Singh S, Liang Q, Chen D et al (2011) Sense through wall human detection using UWB radar. EURASIP J Wirel Commun Netw 2011(1):20CrossRefGoogle Scholar
  9. 9.
    Zetik R, Crabbe S, Krajnak J et al (2006) Detection and localization of persons behind obstacles using M-sequence through-the-wall radar. Proc SPIE 6201:145–156Google Scholar
  10. 10.
    Kumar A, Li Z, Liang Q et al (2014) Experimental study of through-wall human detection using ultra wideband radar sensors. Measurement 47:869–879CrossRefGoogle Scholar
  11. 11.
    Lai CP, Narayanan RM (2010) Ultrawideband random noise radar design for through-wall surveillance. IEEE Trans Aerosp Electron Syst 46(4):1716–1730CrossRefGoogle Scholar
  12. 12.
    Lai CP, Narayanan RM (2005) Through-wall imaging and characterization of human activity using ultrawideband (UWB) random noise radar. Proc SPIE 5778:187Google Scholar
  13. 13.
    Sun J, Li M (2011) Life detection and location methods using UWB impulse radar in a coal mine. Int J Min Sci Technol 21(5):687–691Google Scholar
  14. 14.
    Jovanoska S, Thoma R (2012) Multiple target tracking by a distributed UWB sensor network based on the PHD filter. In: International conference on information fusion, 2012, pp 1095–1102Google Scholar
  15. 15.
    Narayanan RM, Shastry MC, Chen PH et al (2010) Through-the-wall detection of stationary human targets using Doppler radar. Prog Electromagn Res B 20:147–166CrossRefGoogle Scholar
  16. 16.
    Zhang B, Wang W (2013) Through-wall detection of human being with compressed UWB radar data. EURASIP J Wirel Commun Netw 2013(1):162Google Scholar
  17. 17.
    Levitas B, Matuzas J (2006) UWB radar for human being detection behind the wall. In: International radar symposium, 2006. IRS 2006. IEEE, 2007, pp 85–88Google Scholar
  18. 18.
    Marano S, Gifford WM, Wymeersch H, Win MZ (2010) NLOS identification and mitigation for localization based on UWB experimental data. IEEE J Sel Areas Commun 28(7):1026–1035CrossRefGoogle Scholar
  19. 19.
    Chong CC, Watanabe F, Inamura H (2008) NLOS identification and weighted least-squares localization for UWB systems using multipath channel statistics. EURASIP J Adv Signal Process 2008(1):36zbMATHGoogle Scholar
  20. 20.
    Gudmundsson M, El-Kwae EA, Kabuka MR (1998) Edge detection in medical images using a genetic algorithm. IEEE Trans Med Imaging 17(3):469–474CrossRefGoogle Scholar
  21. 21.
    Qu W, Wang J, Zheng J, Li G, Teng J (2006) Fuzzy pattern recognition for stress field of box-type steel structure. Earthq Eng Eng Vibr 26(5):177–182Google Scholar
  22. 22.
    Zhao X, Zhang H (2012) Summary: expression classification algorithm and emotional space model. Int J Digit Content Technol Appl 6(3):37–44MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Tianjin Key Laboratory of Wireless Mobile Communications and Power TransmissionTianjin Normal UniversityTianjinPeople’s Republic of China

Personalised recommendations