A Combined Sensing System for Intrusion Detection Using Anti-Jamming Random Code Signals.

Journal: Sensors (Basel, Switzerland)
PMID:

Abstract

In order to prevent illegal intrusion, theft, and destruction, important places require stable and reliable human intrusion detection technology to maintain security. In this paper, a combined sensing system using anti-jamming random code signals is proposed and demonstrated experimentally to detect the human intruder in the protected area. This sensing system combines the leaky coaxial cable (LCX) sensor and the single-transmitter-double-receivers (STDR) radar sensor. They transmit the orthogonal physical random code signals generated by Boolean chaos as the detection signals. The LCX sensor realizes the early intrusion alarm at the protected area boundary by comparing the correlation traces before and after intrusion. Meanwhile, the STDR radar sensor is used to track the intruder's moving path inside the protected area by correlation ranging and ellipse positioning, as well as recognizing intruder's activities by time-frequency analysis, feature extraction, and support vector machine. The experimental results demonstrate that this combined sensing system not only realizes the early alarm and path tracking for the intruder with the 13 cm positioning accuracy, but also recognizes the intruder's eight activities including squatting, picking up, jumping, waving, walking forward, running forward, walking backward, and running backward with 98.75% average accuracy. Benefiting from the natural randomness and auto-correlation of random code signal, the proposed sensing system is also proved to have a large anti-jamming tolerance of 27.6 dB, which can be used in the complex electromagnetic environment.

Authors

  • Hang Xu
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore. hxu011@e.ntu.edu.sg.
  • Yingxin Li
    Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, No 236 Bai Di Lu Road, Nankai District, Tianjin 300192, China. Electronic address: yingxinli2005@126.com.
  • Cheng Ma
    Department of Statistics, University of Michigan, Ann Arbor, MI, USA.
  • Li Liu
    Metanotitia Inc., Shenzhen, China.
  • Bingjie Wang
    Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education and Shanxi Province, Taiyuan University of Technology, Taiyuan 030024, China.
  • Jingxia Li
    Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education and Shanxi Province, Taiyuan University of Technology, Taiyuan 030024, China.