Sensor fusion based fuzzy rules learning for humanitarian mine detection

Zakarya Zyada, Yasuhiro Kawai, Takayuki Matsuno, Toshio Fukuda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper, a sensor fusion based fuzzy rules for humanitarian demining are presented. A fuzzy learning algorithm for extracting fuzzy fusion rules from experimental data of robot-manipulated ground penetrating radar (GPR) and metal detector (MD) is presented. The inputs to the fuzzy learning algorithm are features extracted from both a GPR and an MD while its output is a set of fuzzy rules. Applying the learnt fuzzy fusion rules and knowing GPR and the MD features of a given scan, it is possible to decide if there is a land mine and its approximate depth underground. The features chosen for this fusion algorithm are the peak amplitude of a processed GPR output signal and the peak value of the cumulative sum of amplitudes of MD output signal for the same scanned area. Experimental test results are presented for verifying the validity of the proposed learnt fuzzy fusion rule base.

Original languageEnglish
Title of host publication2006 SICE-ICASE International Joint Conference
Pages1860-1865
Number of pages6
DOIs
Publication statusPublished - Dec 1 2006
Externally publishedYes
Event2006 SICE-ICASE International Joint Conference - Busan, Korea, Republic of
Duration: Oct 18 2006Oct 21 2006

Publication series

Name2006 SICE-ICASE International Joint Conference

Other

Other2006 SICE-ICASE International Joint Conference
CountryKorea, Republic of
CityBusan
Period10/18/0610/21/06

Keywords

  • Fuzzy learning
  • Humanitarian demining
  • Sensor fusion

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Zyada, Z., Kawai, Y., Matsuno, T., & Fukuda, T. (2006). Sensor fusion based fuzzy rules learning for humanitarian mine detection. In 2006 SICE-ICASE International Joint Conference (pp. 1860-1865). [4108987] (2006 SICE-ICASE International Joint Conference). https://doi.org/10.1109/SICE.2006.315804